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update of api docstrings across coach and tutorials [WIP] (#91)
* updating the documentation website * adding the built docs * update of api docstrings across coach and tutorials 0-2 * added some missing api documentation * New Sphinx based documentation
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Itai Caspi
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<h1>Source code for rl_coach.memories.episodic.episodic_experience_replay</h1><div class="highlight"><pre>
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<span></span><span class="c1">#</span>
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<span class="c1"># Copyright (c) 2017 Intel Corporation</span>
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<span class="c1">#</span>
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<span class="c1"># Licensed under the Apache License, Version 2.0 (the "License");</span>
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<span class="c1"># you may not use this file except in compliance with the License.</span>
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<span class="c1"># You may obtain a copy of the License at</span>
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<span class="c1">#</span>
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<span class="c1"># http://www.apache.org/licenses/LICENSE-2.0</span>
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<span class="c1">#</span>
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<span class="c1"># Unless required by applicable law or agreed to in writing, software</span>
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<span class="c1"># distributed under the License is distributed on an "AS IS" BASIS,</span>
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<span class="c1"># WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
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<span class="c1"># See the License for the specific language governing permissions and</span>
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<span class="c1"># limitations under the License.</span>
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<span class="c1">#</span>
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<span class="kn">from</span> <span class="nn">typing</span> <span class="k">import</span> <span class="n">List</span><span class="p">,</span> <span class="n">Tuple</span><span class="p">,</span> <span class="n">Union</span><span class="p">,</span> <span class="n">Dict</span><span class="p">,</span> <span class="n">Any</span>
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<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
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<span class="kn">from</span> <span class="nn">rl_coach.core_types</span> <span class="k">import</span> <span class="n">Transition</span><span class="p">,</span> <span class="n">Episode</span>
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<span class="kn">from</span> <span class="nn">rl_coach.memories.memory</span> <span class="k">import</span> <span class="n">Memory</span><span class="p">,</span> <span class="n">MemoryGranularity</span><span class="p">,</span> <span class="n">MemoryParameters</span>
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<span class="kn">from</span> <span class="nn">rl_coach.utils</span> <span class="k">import</span> <span class="n">ReaderWriterLock</span>
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<span class="k">class</span> <span class="nc">EpisodicExperienceReplayParameters</span><span class="p">(</span><span class="n">MemoryParameters</span><span class="p">):</span>
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<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
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<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">max_size</span> <span class="o">=</span> <span class="p">(</span><span class="n">MemoryGranularity</span><span class="o">.</span><span class="n">Transitions</span><span class="p">,</span> <span class="mi">1000000</span><span class="p">)</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">n_step</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
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<span class="nd">@property</span>
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<span class="k">def</span> <span class="nf">path</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
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<span class="k">return</span> <span class="s1">'rl_coach.memories.episodic.episodic_experience_replay:EpisodicExperienceReplay'</span>
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<div class="viewcode-block" id="EpisodicExperienceReplay"><a class="viewcode-back" href="../../../../components/memories/index.html#rl_coach.memories.episodic.EpisodicExperienceReplay">[docs]</a><span class="k">class</span> <span class="nc">EpisodicExperienceReplay</span><span class="p">(</span><span class="n">Memory</span><span class="p">):</span>
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<span class="sd">"""</span>
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<span class="sd"> A replay buffer that stores episodes of transitions. The additional structure allows performing various</span>
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<span class="sd"> calculations of total return and other values that depend on the sequential behavior of the transitions</span>
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<span class="sd"> in the episode.</span>
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<span class="sd"> """</span>
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<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">max_size</span><span class="p">:</span> <span class="n">Tuple</span><span class="p">[</span><span class="n">MemoryGranularity</span><span class="p">,</span> <span class="nb">int</span><span class="p">]</span><span class="o">=</span><span class="p">(</span><span class="n">MemoryGranularity</span><span class="o">.</span><span class="n">Transitions</span><span class="p">,</span> <span class="mi">1000000</span><span class="p">),</span> <span class="n">n_step</span><span class="o">=-</span><span class="mi">1</span><span class="p">):</span>
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||||
<span class="sd">"""</span>
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||||
<span class="sd"> :param max_size: the maximum number of transitions or episodes to hold in the memory</span>
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<span class="sd"> """</span>
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||||
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">max_size</span><span class="p">)</span>
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||||
<span class="bp">self</span><span class="o">.</span><span class="n">n_step</span> <span class="o">=</span> <span class="n">n_step</span>
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||||
<span class="bp">self</span><span class="o">.</span><span class="n">_buffer</span> <span class="o">=</span> <span class="p">[</span><span class="n">Episode</span><span class="p">(</span><span class="n">n_step</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">n_step</span><span class="p">)]</span> <span class="c1"># list of episodes</span>
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||||
<span class="bp">self</span><span class="o">.</span><span class="n">transitions</span> <span class="o">=</span> <span class="p">[]</span>
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||||
<span class="bp">self</span><span class="o">.</span><span class="n">_length</span> <span class="o">=</span> <span class="mi">1</span> <span class="c1"># the episodic replay buffer starts with a single empty episode</span>
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||||
<span class="bp">self</span><span class="o">.</span><span class="n">_num_transitions</span> <span class="o">=</span> <span class="mi">0</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">_num_transitions_in_complete_episodes</span> <span class="o">=</span> <span class="mi">0</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">reader_writer_lock</span> <span class="o">=</span> <span class="n">ReaderWriterLock</span><span class="p">()</span>
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<span class="k">def</span> <span class="nf">length</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">lock</span><span class="p">:</span> <span class="nb">bool</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span> <span class="o">-></span> <span class="nb">int</span><span class="p">:</span>
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<span class="sd">"""</span>
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||||
<span class="sd"> Get the number of episodes in the ER (even if they are not complete)</span>
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<span class="sd"> """</span>
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<span class="n">length</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_length</span>
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<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_length</span> <span class="ow">is</span> <span class="ow">not</span> <span class="mi">0</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">_buffer</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">is_empty</span><span class="p">():</span>
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<span class="n">length</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_length</span> <span class="o">-</span> <span class="mi">1</span>
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<span class="k">return</span> <span class="n">length</span>
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<span class="k">def</span> <span class="nf">num_complete_episodes</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
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<span class="sd">""" Get the number of complete episodes in ER """</span>
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<span class="n">length</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_length</span> <span class="o">-</span> <span class="mi">1</span>
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<span class="k">return</span> <span class="n">length</span>
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||||
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||||
<span class="k">def</span> <span class="nf">num_transitions</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
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<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_num_transitions</span>
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<span class="k">def</span> <span class="nf">num_transitions_in_complete_episodes</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
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<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_num_transitions_in_complete_episodes</span>
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||||
<span class="k">def</span> <span class="nf">sample</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">size</span><span class="p">:</span> <span class="nb">int</span><span class="p">)</span> <span class="o">-></span> <span class="n">List</span><span class="p">[</span><span class="n">Transition</span><span class="p">]:</span>
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||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Sample a batch of transitions form the replay buffer. If the requested size is larger than the number</span>
|
||||
<span class="sd"> of samples available in the replay buffer then the batch will return empty.</span>
|
||||
<span class="sd"> :param size: the size of the batch to sample</span>
|
||||
<span class="sd"> :return: a batch (list) of selected transitions from the replay buffer</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">reader_writer_lock</span><span class="o">.</span><span class="n">lock_writing</span><span class="p">()</span>
|
||||
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||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_complete_episodes</span><span class="p">()</span> <span class="o">>=</span> <span class="mi">1</span><span class="p">:</span>
|
||||
<span class="n">transitions_idx</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">num_transitions_in_complete_episodes</span><span class="p">(),</span> <span class="n">size</span><span class="o">=</span><span class="n">size</span><span class="p">)</span>
|
||||
<span class="n">batch</span> <span class="o">=</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">transitions</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">transitions_idx</span><span class="p">]</span>
|
||||
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"The episodic replay buffer cannot be sampled since there are no complete episodes yet. "</span>
|
||||
<span class="s2">"There is currently 1 episodes with </span><span class="si">{}</span><span class="s2"> transitions"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_buffer</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">length</span><span class="p">()))</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">reader_writer_lock</span><span class="o">.</span><span class="n">release_writing</span><span class="p">()</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">batch</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">_enforce_max_length</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-></span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Make sure that the size of the replay buffer does not pass the maximum size allowed.</span>
|
||||
<span class="sd"> If it passes the max size, the oldest episode in the replay buffer will be removed.</span>
|
||||
<span class="sd"> :return: None</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">granularity</span><span class="p">,</span> <span class="n">size</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_size</span>
|
||||
<span class="k">if</span> <span class="n">granularity</span> <span class="o">==</span> <span class="n">MemoryGranularity</span><span class="o">.</span><span class="n">Transitions</span><span class="p">:</span>
|
||||
<span class="k">while</span> <span class="n">size</span> <span class="o">!=</span> <span class="mi">0</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_transitions</span><span class="p">()</span> <span class="o">></span> <span class="n">size</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_remove_episode</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
|
||||
<span class="k">elif</span> <span class="n">granularity</span> <span class="o">==</span> <span class="n">MemoryGranularity</span><span class="o">.</span><span class="n">Episodes</span><span class="p">:</span>
|
||||
<span class="k">while</span> <span class="bp">self</span><span class="o">.</span><span class="n">length</span><span class="p">()</span> <span class="o">></span> <span class="n">size</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_remove_episode</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">_update_episode</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">episode</span><span class="p">:</span> <span class="n">Episode</span><span class="p">)</span> <span class="o">-></span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="n">episode</span><span class="o">.</span><span class="n">update_transitions_rewards_and_bootstrap_data</span><span class="p">()</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">verify_last_episode_is_closed</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-></span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Verify that there is no open episodes in the replay buffer</span>
|
||||
<span class="sd"> :return: None</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">reader_writer_lock</span><span class="o">.</span><span class="n">lock_writing_and_reading</span><span class="p">()</span>
|
||||
|
||||
<span class="n">last_episode</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">last_episode</span> <span class="ow">and</span> <span class="n">last_episode</span><span class="o">.</span><span class="n">length</span><span class="p">()</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">close_last_episode</span><span class="p">(</span><span class="n">lock</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">reader_writer_lock</span><span class="o">.</span><span class="n">release_writing_and_reading</span><span class="p">()</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">close_last_episode</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">lock</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="o">-></span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Close the last episode in the replay buffer and open a new one</span>
|
||||
<span class="sd"> :return: None</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">if</span> <span class="n">lock</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">reader_writer_lock</span><span class="o">.</span><span class="n">lock_writing_and_reading</span><span class="p">()</span>
|
||||
|
||||
<span class="n">last_episode</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_buffer</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_num_transitions_in_complete_episodes</span> <span class="o">+=</span> <span class="n">last_episode</span><span class="o">.</span><span class="n">length</span><span class="p">()</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_length</span> <span class="o">+=</span> <span class="mi">1</span>
|
||||
|
||||
<span class="c1"># create a new Episode for the next transitions to be placed into</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_buffer</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">Episode</span><span class="p">(</span><span class="n">n_step</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">n_step</span><span class="p">))</span>
|
||||
|
||||
<span class="c1"># if update episode adds to the buffer, a new Episode needs to be ready first</span>
|
||||
<span class="c1"># it would be better if this were less state full</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_update_episode</span><span class="p">(</span><span class="n">last_episode</span><span class="p">)</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_enforce_max_length</span><span class="p">()</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">lock</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">reader_writer_lock</span><span class="o">.</span><span class="n">release_writing_and_reading</span><span class="p">()</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">store</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">transition</span><span class="p">:</span> <span class="n">Transition</span><span class="p">)</span> <span class="o">-></span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Store a new transition in the memory. If the transition game_over flag is on, this closes the episode and</span>
|
||||
<span class="sd"> creates a new empty episode.</span>
|
||||
<span class="sd"> Warning! using the episodic memory by storing individual transitions instead of episodes will use the default</span>
|
||||
<span class="sd"> Episode class parameters in order to create new episodes.</span>
|
||||
<span class="sd"> :param transition: a transition to store</span>
|
||||
<span class="sd"> :return: None</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="c1"># Calling super.store() so that in case a memory backend is used, the memory backend can store this transition.</span>
|
||||
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">store</span><span class="p">(</span><span class="n">transition</span><span class="p">)</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">reader_writer_lock</span><span class="o">.</span><span class="n">lock_writing_and_reading</span><span class="p">()</span>
|
||||
|
||||
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_buffer</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_buffer</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">Episode</span><span class="p">(</span><span class="n">n_step</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">n_step</span><span class="p">))</span>
|
||||
<span class="n">last_episode</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_buffer</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
|
||||
<span class="n">last_episode</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="n">transition</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">transitions</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">transition</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_num_transitions</span> <span class="o">+=</span> <span class="mi">1</span>
|
||||
<span class="k">if</span> <span class="n">transition</span><span class="o">.</span><span class="n">game_over</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">close_last_episode</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_enforce_max_length</span><span class="p">()</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">reader_writer_lock</span><span class="o">.</span><span class="n">release_writing_and_reading</span><span class="p">()</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">store_episode</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">episode</span><span class="p">:</span> <span class="n">Episode</span><span class="p">,</span> <span class="n">lock</span><span class="p">:</span> <span class="nb">bool</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="o">-></span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Store a new episode in the memory.</span>
|
||||
<span class="sd"> :param episode: the new episode to store</span>
|
||||
<span class="sd"> :return: None</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="c1"># Calling super.store() so that in case a memory backend is used, the memory backend can store this episode.</span>
|
||||
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">store_episode</span><span class="p">(</span><span class="n">episode</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">lock</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">reader_writer_lock</span><span class="o">.</span><span class="n">lock_writing_and_reading</span><span class="p">()</span>
|
||||
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_buffer</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">length</span><span class="p">()</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_buffer</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">episode</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_buffer</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">episode</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">transitions</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">episode</span><span class="o">.</span><span class="n">transitions</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_num_transitions</span> <span class="o">+=</span> <span class="n">episode</span><span class="o">.</span><span class="n">length</span><span class="p">()</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">close_last_episode</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">lock</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">reader_writer_lock</span><span class="o">.</span><span class="n">release_writing_and_reading</span><span class="p">()</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">get_episode</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">episode_index</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">lock</span><span class="p">:</span> <span class="nb">bool</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="o">-></span> <span class="n">Union</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="n">Episode</span><span class="p">]:</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Returns the episode in the given index. If the episode does not exist, returns None instead.</span>
|
||||
<span class="sd"> :param episode_index: the index of the episode to return</span>
|
||||
<span class="sd"> :return: the corresponding episode</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">if</span> <span class="n">lock</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">reader_writer_lock</span><span class="o">.</span><span class="n">lock_writing</span><span class="p">()</span>
|
||||
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">length</span><span class="p">()</span> <span class="o">==</span> <span class="mi">0</span> <span class="ow">or</span> <span class="n">episode_index</span> <span class="o">>=</span> <span class="bp">self</span><span class="o">.</span><span class="n">length</span><span class="p">():</span>
|
||||
<span class="n">episode</span> <span class="o">=</span> <span class="kc">None</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">episode</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_buffer</span><span class="p">[</span><span class="n">episode_index</span><span class="p">]</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">lock</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">reader_writer_lock</span><span class="o">.</span><span class="n">release_writing</span><span class="p">()</span>
|
||||
<span class="k">return</span> <span class="n">episode</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">_remove_episode</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">episode_index</span><span class="p">:</span> <span class="nb">int</span><span class="p">)</span> <span class="o">-></span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Remove the episode in the given index (even if it is not complete yet)</span>
|
||||
<span class="sd"> :param episode_index: the index of the episode to remove</span>
|
||||
<span class="sd"> :return: None</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_buffer</span><span class="p">)</span> <span class="o">></span> <span class="n">episode_index</span><span class="p">:</span>
|
||||
<span class="n">episode_length</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_buffer</span><span class="p">[</span><span class="n">episode_index</span><span class="p">]</span><span class="o">.</span><span class="n">length</span><span class="p">()</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_length</span> <span class="o">-=</span> <span class="mi">1</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_num_transitions</span> <span class="o">-=</span> <span class="n">episode_length</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_num_transitions_in_complete_episodes</span> <span class="o">-=</span> <span class="n">episode_length</span>
|
||||
<span class="k">del</span> <span class="bp">self</span><span class="o">.</span><span class="n">transitions</span><span class="p">[:</span><span class="n">episode_length</span><span class="p">]</span>
|
||||
<span class="k">del</span> <span class="bp">self</span><span class="o">.</span><span class="n">_buffer</span><span class="p">[</span><span class="n">episode_index</span><span class="p">]</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">remove_episode</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">episode_index</span><span class="p">:</span> <span class="nb">int</span><span class="p">)</span> <span class="o">-></span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Remove the episode in the given index (even if it is not complete yet)</span>
|
||||
<span class="sd"> :param episode_index: the index of the episode to remove</span>
|
||||
<span class="sd"> :return: None</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">reader_writer_lock</span><span class="o">.</span><span class="n">lock_writing_and_reading</span><span class="p">()</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_remove_episode</span><span class="p">(</span><span class="n">episode_index</span><span class="p">)</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">reader_writer_lock</span><span class="o">.</span><span class="n">release_writing_and_reading</span><span class="p">()</span>
|
||||
|
||||
<span class="c1"># for API compatibility</span>
|
||||
<span class="k">def</span> <span class="nf">get</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">episode_index</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">lock</span><span class="p">:</span> <span class="nb">bool</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="o">-></span> <span class="n">Union</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="n">Episode</span><span class="p">]:</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Returns the episode in the given index. If the episode does not exist, returns None instead.</span>
|
||||
<span class="sd"> :param episode_index: the index of the episode to return</span>
|
||||
<span class="sd"> :return: the corresponding episode</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_episode</span><span class="p">(</span><span class="n">episode_index</span><span class="p">,</span> <span class="n">lock</span><span class="p">)</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">get_last_complete_episode</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-></span> <span class="n">Union</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="n">Episode</span><span class="p">]:</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Returns the last complete episode in the memory or None if there are no complete episodes</span>
|
||||
<span class="sd"> :return: None or the last complete episode</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">reader_writer_lock</span><span class="o">.</span><span class="n">lock_writing</span><span class="p">()</span>
|
||||
|
||||
<span class="n">last_complete_episode_index</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_complete_episodes</span><span class="p">()</span> <span class="o">-</span> <span class="mi">1</span>
|
||||
<span class="n">episode</span> <span class="o">=</span> <span class="kc">None</span>
|
||||
<span class="k">if</span> <span class="n">last_complete_episode_index</span> <span class="o">>=</span> <span class="mi">0</span><span class="p">:</span>
|
||||
<span class="n">episode</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">last_complete_episode_index</span><span class="p">)</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">reader_writer_lock</span><span class="o">.</span><span class="n">release_writing</span><span class="p">()</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">episode</span>
|
||||
|
||||
<span class="c1"># for API compatibility</span>
|
||||
<span class="k">def</span> <span class="nf">remove</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">episode_index</span><span class="p">:</span> <span class="nb">int</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Remove the episode in the given index (even if it is not complete yet)</span>
|
||||
<span class="sd"> :param episode_index: the index of the episode to remove</span>
|
||||
<span class="sd"> :return: None</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">remove_episode</span><span class="p">(</span><span class="n">episode_index</span><span class="p">)</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">clean</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-></span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Clean the memory by removing all the episodes</span>
|
||||
<span class="sd"> :return: None</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">reader_writer_lock</span><span class="o">.</span><span class="n">lock_writing_and_reading</span><span class="p">()</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">transitions</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_buffer</span> <span class="o">=</span> <span class="p">[</span><span class="n">Episode</span><span class="p">(</span><span class="n">n_step</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">n_step</span><span class="p">)]</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_length</span> <span class="o">=</span> <span class="mi">1</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_num_transitions</span> <span class="o">=</span> <span class="mi">0</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_num_transitions_in_complete_episodes</span> <span class="o">=</span> <span class="mi">0</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">reader_writer_lock</span><span class="o">.</span><span class="n">release_writing_and_reading</span><span class="p">()</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">mean_reward</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-></span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Get the mean reward in the replay buffer</span>
|
||||
<span class="sd"> :return: the mean reward</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">reader_writer_lock</span><span class="o">.</span><span class="n">lock_writing</span><span class="p">()</span>
|
||||
|
||||
<span class="n">mean</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">([</span><span class="n">transition</span><span class="o">.</span><span class="n">reward</span> <span class="k">for</span> <span class="n">transition</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">transitions</span><span class="p">])</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">reader_writer_lock</span><span class="o">.</span><span class="n">release_writing</span><span class="p">()</span>
|
||||
<span class="k">return</span> <span class="n">mean</span></div>
|
||||
</pre></div>
|
||||
|
||||
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|
||||
|
||||
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<h1>Source code for rl_coach.memories.episodic.episodic_hindsight_experience_replay</h1><div class="highlight"><pre>
|
||||
<span></span><span class="c1">#</span>
|
||||
<span class="c1"># Copyright (c) 2017 Intel Corporation</span>
|
||||
<span class="c1">#</span>
|
||||
<span class="c1"># Licensed under the Apache License, Version 2.0 (the "License");</span>
|
||||
<span class="c1"># you may not use this file except in compliance with the License.</span>
|
||||
<span class="c1"># You may obtain a copy of the License at</span>
|
||||
<span class="c1">#</span>
|
||||
<span class="c1"># http://www.apache.org/licenses/LICENSE-2.0</span>
|
||||
<span class="c1">#</span>
|
||||
<span class="c1"># Unless required by applicable law or agreed to in writing, software</span>
|
||||
<span class="c1"># distributed under the License is distributed on an "AS IS" BASIS,</span>
|
||||
<span class="c1"># WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
|
||||
<span class="c1"># See the License for the specific language governing permissions and</span>
|
||||
<span class="c1"># limitations under the License.</span>
|
||||
<span class="c1">#</span>
|
||||
|
||||
<span class="kn">import</span> <span class="nn">copy</span>
|
||||
<span class="kn">from</span> <span class="nn">enum</span> <span class="k">import</span> <span class="n">Enum</span>
|
||||
<span class="kn">from</span> <span class="nn">typing</span> <span class="k">import</span> <span class="n">Tuple</span><span class="p">,</span> <span class="n">List</span>
|
||||
|
||||
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
|
||||
|
||||
<span class="kn">from</span> <span class="nn">rl_coach.core_types</span> <span class="k">import</span> <span class="n">Episode</span><span class="p">,</span> <span class="n">Transition</span>
|
||||
<span class="kn">from</span> <span class="nn">rl_coach.memories.episodic.episodic_experience_replay</span> <span class="k">import</span> <span class="n">EpisodicExperienceReplayParameters</span><span class="p">,</span> \
|
||||
<span class="n">EpisodicExperienceReplay</span>
|
||||
<span class="kn">from</span> <span class="nn">rl_coach.memories.non_episodic.experience_replay</span> <span class="k">import</span> <span class="n">MemoryGranularity</span>
|
||||
<span class="kn">from</span> <span class="nn">rl_coach.spaces</span> <span class="k">import</span> <span class="n">GoalsSpace</span>
|
||||
|
||||
|
||||
<span class="k">class</span> <span class="nc">HindsightGoalSelectionMethod</span><span class="p">(</span><span class="n">Enum</span><span class="p">):</span>
|
||||
<span class="n">Future</span> <span class="o">=</span> <span class="mi">0</span>
|
||||
<span class="n">Final</span> <span class="o">=</span> <span class="mi">1</span>
|
||||
<span class="n">Episode</span> <span class="o">=</span> <span class="mi">2</span>
|
||||
<span class="n">Random</span> <span class="o">=</span> <span class="mi">3</span>
|
||||
|
||||
|
||||
<span class="k">class</span> <span class="nc">EpisodicHindsightExperienceReplayParameters</span><span class="p">(</span><span class="n">EpisodicExperienceReplayParameters</span><span class="p">):</span>
|
||||
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">hindsight_transitions_per_regular_transition</span> <span class="o">=</span> <span class="kc">None</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">hindsight_goal_selection_method</span> <span class="o">=</span> <span class="kc">None</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">goals_space</span> <span class="o">=</span> <span class="kc">None</span>
|
||||
|
||||
<span class="nd">@property</span>
|
||||
<span class="k">def</span> <span class="nf">path</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="k">return</span> <span class="s1">'rl_coach.memories.episodic.episodic_hindsight_experience_replay:EpisodicHindsightExperienceReplay'</span>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="EpisodicHindsightExperienceReplay"><a class="viewcode-back" href="../../../../components/memories/index.html#rl_coach.memories.episodic.EpisodicHindsightExperienceReplay">[docs]</a><span class="k">class</span> <span class="nc">EpisodicHindsightExperienceReplay</span><span class="p">(</span><span class="n">EpisodicExperienceReplay</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Implements Hindsight Experience Replay as described in the following paper: https://arxiv.org/pdf/1707.01495.pdf</span>
|
||||
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">max_size</span><span class="p">:</span> <span class="n">Tuple</span><span class="p">[</span><span class="n">MemoryGranularity</span><span class="p">,</span> <span class="nb">int</span><span class="p">],</span>
|
||||
<span class="n">hindsight_transitions_per_regular_transition</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
|
||||
<span class="n">hindsight_goal_selection_method</span><span class="p">:</span> <span class="n">HindsightGoalSelectionMethod</span><span class="p">,</span>
|
||||
<span class="n">goals_space</span><span class="p">:</span> <span class="n">GoalsSpace</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> :param max_size: The maximum size of the memory. should be defined in a granularity of Transitions</span>
|
||||
<span class="sd"> :param hindsight_transitions_per_regular_transition: The number of hindsight artificial transitions to generate</span>
|
||||
<span class="sd"> for each actual transition</span>
|
||||
<span class="sd"> :param hindsight_goal_selection_method: The method that will be used for generating the goals for the</span>
|
||||
<span class="sd"> hindsight transitions. Should be one of HindsightGoalSelectionMethod</span>
|
||||
<span class="sd"> :param goals_space: A GoalsSpace which defines the base properties of the goals space</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">max_size</span><span class="p">)</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">hindsight_transitions_per_regular_transition</span> <span class="o">=</span> <span class="n">hindsight_transitions_per_regular_transition</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">hindsight_goal_selection_method</span> <span class="o">=</span> <span class="n">hindsight_goal_selection_method</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">goals_space</span> <span class="o">=</span> <span class="n">goals_space</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">last_episode_start_idx</span> <span class="o">=</span> <span class="mi">0</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">_sample_goal</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">episode_transitions</span><span class="p">:</span> <span class="n">List</span><span class="p">,</span> <span class="n">transition_index</span><span class="p">:</span> <span class="nb">int</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Sample a single goal state according to the sampling method</span>
|
||||
<span class="sd"> :param episode_transitions: a list of all the transitions in the current episode</span>
|
||||
<span class="sd"> :param transition_index: the transition to start sampling from</span>
|
||||
<span class="sd"> :return: a goal corresponding to the sampled state</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">hindsight_goal_selection_method</span> <span class="o">==</span> <span class="n">HindsightGoalSelectionMethod</span><span class="o">.</span><span class="n">Future</span><span class="p">:</span>
|
||||
<span class="c1"># states that were observed in the same episode after the transition that is being replayed</span>
|
||||
<span class="n">selected_transition</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="n">episode_transitions</span><span class="p">[</span><span class="n">transition_index</span><span class="o">+</span><span class="mi">1</span><span class="p">:])</span>
|
||||
<span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">hindsight_goal_selection_method</span> <span class="o">==</span> <span class="n">HindsightGoalSelectionMethod</span><span class="o">.</span><span class="n">Final</span><span class="p">:</span>
|
||||
<span class="c1"># the final state in the episode</span>
|
||||
<span class="n">selected_transition</span> <span class="o">=</span> <span class="n">episode_transitions</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
|
||||
<span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">hindsight_goal_selection_method</span> <span class="o">==</span> <span class="n">HindsightGoalSelectionMethod</span><span class="o">.</span><span class="n">Episode</span><span class="p">:</span>
|
||||
<span class="c1"># a random state from the episode</span>
|
||||
<span class="n">selected_transition</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="n">episode_transitions</span><span class="p">)</span>
|
||||
<span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">hindsight_goal_selection_method</span> <span class="o">==</span> <span class="n">HindsightGoalSelectionMethod</span><span class="o">.</span><span class="n">Random</span><span class="p">:</span>
|
||||
<span class="c1"># a random state from the entire replay buffer</span>
|
||||
<span class="n">selected_transition</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">transitions</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"Invalid goal selection method was used for the hindsight goal selection"</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">goals_space</span><span class="o">.</span><span class="n">goal_from_state</span><span class="p">(</span><span class="n">selected_transition</span><span class="o">.</span><span class="n">state</span><span class="p">)</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">_sample_goals</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">episode_transitions</span><span class="p">:</span> <span class="n">List</span><span class="p">,</span> <span class="n">transition_index</span><span class="p">:</span> <span class="nb">int</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Sample a batch of goal states according to the sampling method</span>
|
||||
<span class="sd"> :param episode_transitions: a list of all the transitions in the current episode</span>
|
||||
<span class="sd"> :param transition_index: the transition to start sampling from</span>
|
||||
<span class="sd"> :return: a goal corresponding to the sampled state</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="p">[</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_sample_goal</span><span class="p">(</span><span class="n">episode_transitions</span><span class="p">,</span> <span class="n">transition_index</span><span class="p">)</span>
|
||||
<span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">hindsight_transitions_per_regular_transition</span><span class="p">)</span>
|
||||
<span class="p">]</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">store_episode</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">episode</span><span class="p">:</span> <span class="n">Episode</span><span class="p">,</span> <span class="n">lock</span><span class="p">:</span> <span class="nb">bool</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="o">-></span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="c1"># generate hindsight transitions only when an episode is finished</span>
|
||||
<span class="n">last_episode_transitions</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">episode</span><span class="o">.</span><span class="n">transitions</span><span class="p">)</span>
|
||||
|
||||
<span class="c1"># cannot create a future hindsight goal in the last transition of an episode</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">hindsight_goal_selection_method</span> <span class="o">==</span> <span class="n">HindsightGoalSelectionMethod</span><span class="o">.</span><span class="n">Future</span><span class="p">:</span>
|
||||
<span class="n">relevant_base_transitions</span> <span class="o">=</span> <span class="n">last_episode_transitions</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">relevant_base_transitions</span> <span class="o">=</span> <span class="n">last_episode_transitions</span>
|
||||
|
||||
<span class="c1"># for each transition in the last episode, create a set of hindsight transitions</span>
|
||||
<span class="k">for</span> <span class="n">transition_index</span><span class="p">,</span> <span class="n">transition</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">relevant_base_transitions</span><span class="p">):</span>
|
||||
<span class="n">sampled_goals</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_sample_goals</span><span class="p">(</span><span class="n">last_episode_transitions</span><span class="p">,</span> <span class="n">transition_index</span><span class="p">)</span>
|
||||
<span class="k">for</span> <span class="n">goal</span> <span class="ow">in</span> <span class="n">sampled_goals</span><span class="p">:</span>
|
||||
<span class="n">hindsight_transition</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">transition</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">hindsight_transition</span><span class="o">.</span><span class="n">state</span><span class="p">[</span><span class="s1">'desired_goal'</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span> <span class="o">!=</span> <span class="n">goal</span><span class="o">.</span><span class="n">shape</span><span class="p">:</span>
|
||||
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">((</span>
|
||||
<span class="s1">'goal shape </span><span class="si">{goal_shape}</span><span class="s1"> already in transition is '</span>
|
||||
<span class="s1">'different than the one sampled as a hindsight goal '</span>
|
||||
<span class="s1">'</span><span class="si">{hindsight_goal_shape}</span><span class="s1">.'</span>
|
||||
<span class="p">)</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
|
||||
<span class="n">goal_shape</span><span class="o">=</span><span class="n">hindsight_transition</span><span class="o">.</span><span class="n">state</span><span class="p">[</span><span class="s1">'desired_goal'</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span>
|
||||
<span class="n">hindsight_goal_shape</span><span class="o">=</span><span class="n">goal</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span>
|
||||
<span class="p">))</span>
|
||||
|
||||
<span class="c1"># update the goal in the transition</span>
|
||||
<span class="n">hindsight_transition</span><span class="o">.</span><span class="n">state</span><span class="p">[</span><span class="s1">'desired_goal'</span><span class="p">]</span> <span class="o">=</span> <span class="n">goal</span>
|
||||
<span class="n">hindsight_transition</span><span class="o">.</span><span class="n">next_state</span><span class="p">[</span><span class="s1">'desired_goal'</span><span class="p">]</span> <span class="o">=</span> <span class="n">goal</span>
|
||||
|
||||
<span class="c1"># update the reward and terminal signal according to the goal</span>
|
||||
<span class="n">hindsight_transition</span><span class="o">.</span><span class="n">reward</span><span class="p">,</span> <span class="n">hindsight_transition</span><span class="o">.</span><span class="n">game_over</span> <span class="o">=</span> \
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">goals_space</span><span class="o">.</span><span class="n">get_reward_for_goal_and_state</span><span class="p">(</span><span class="n">goal</span><span class="p">,</span> <span class="n">hindsight_transition</span><span class="o">.</span><span class="n">next_state</span><span class="p">)</span>
|
||||
|
||||
<span class="n">hindsight_transition</span><span class="o">.</span><span class="n">n_step_discounted_rewards</span> <span class="o">=</span> <span class="kc">None</span>
|
||||
<span class="n">episode</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="n">hindsight_transition</span><span class="p">)</span>
|
||||
|
||||
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">store_episode</span><span class="p">(</span><span class="n">episode</span><span class="p">)</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">store</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">transition</span><span class="p">:</span> <span class="n">Transition</span><span class="p">):</span>
|
||||
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"An episodic HER cannot store a single transition. Only full episodes are to be stored."</span><span class="p">)</span></div>
|
||||
</pre></div>
|
||||
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<h1>Source code for rl_coach.memories.episodic.episodic_hrl_hindsight_experience_replay</h1><div class="highlight"><pre>
|
||||
<span></span><span class="c1">#</span>
|
||||
<span class="c1"># Copyright (c) 2017 Intel Corporation</span>
|
||||
<span class="c1">#</span>
|
||||
<span class="c1"># Licensed under the Apache License, Version 2.0 (the "License");</span>
|
||||
<span class="c1"># you may not use this file except in compliance with the License.</span>
|
||||
<span class="c1"># You may obtain a copy of the License at</span>
|
||||
<span class="c1">#</span>
|
||||
<span class="c1"># http://www.apache.org/licenses/LICENSE-2.0</span>
|
||||
<span class="c1">#</span>
|
||||
<span class="c1"># Unless required by applicable law or agreed to in writing, software</span>
|
||||
<span class="c1"># distributed under the License is distributed on an "AS IS" BASIS,</span>
|
||||
<span class="c1"># WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
|
||||
<span class="c1"># See the License for the specific language governing permissions and</span>
|
||||
<span class="c1"># limitations under the License.</span>
|
||||
<span class="c1">#</span>
|
||||
|
||||
<span class="kn">from</span> <span class="nn">typing</span> <span class="k">import</span> <span class="n">Tuple</span>
|
||||
|
||||
<span class="kn">from</span> <span class="nn">rl_coach.core_types</span> <span class="k">import</span> <span class="n">Episode</span><span class="p">,</span> <span class="n">Transition</span>
|
||||
<span class="kn">from</span> <span class="nn">rl_coach.memories.episodic.episodic_hindsight_experience_replay</span> <span class="k">import</span> <span class="n">HindsightGoalSelectionMethod</span><span class="p">,</span> \
|
||||
<span class="n">EpisodicHindsightExperienceReplay</span><span class="p">,</span> <span class="n">EpisodicHindsightExperienceReplayParameters</span>
|
||||
<span class="kn">from</span> <span class="nn">rl_coach.memories.non_episodic.experience_replay</span> <span class="k">import</span> <span class="n">MemoryGranularity</span>
|
||||
<span class="kn">from</span> <span class="nn">rl_coach.spaces</span> <span class="k">import</span> <span class="n">GoalsSpace</span>
|
||||
|
||||
|
||||
<span class="k">class</span> <span class="nc">EpisodicHRLHindsightExperienceReplayParameters</span><span class="p">(</span><span class="n">EpisodicHindsightExperienceReplayParameters</span><span class="p">):</span>
|
||||
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
|
||||
|
||||
<span class="nd">@property</span>
|
||||
<span class="k">def</span> <span class="nf">path</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="k">return</span> <span class="s1">'rl_coach.memories.episodic.episodic_hrl_hindsight_experience_replay:EpisodicHRLHindsightExperienceReplay'</span>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="EpisodicHRLHindsightExperienceReplay"><a class="viewcode-back" href="../../../../components/memories/index.html#rl_coach.memories.episodic.EpisodicHRLHindsightExperienceReplay">[docs]</a><span class="k">class</span> <span class="nc">EpisodicHRLHindsightExperienceReplay</span><span class="p">(</span><span class="n">EpisodicHindsightExperienceReplay</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Implements HRL Hindsight Experience Replay as described in the following paper: https://arxiv.org/abs/1805.08180</span>
|
||||
|
||||
<span class="sd"> This is the memory you should use if you want a shared hindsight experience replay buffer between multiple workers</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">max_size</span><span class="p">:</span> <span class="n">Tuple</span><span class="p">[</span><span class="n">MemoryGranularity</span><span class="p">,</span> <span class="nb">int</span><span class="p">],</span>
|
||||
<span class="n">hindsight_transitions_per_regular_transition</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
|
||||
<span class="n">hindsight_goal_selection_method</span><span class="p">:</span> <span class="n">HindsightGoalSelectionMethod</span><span class="p">,</span>
|
||||
<span class="n">goals_space</span><span class="p">:</span> <span class="n">GoalsSpace</span><span class="p">,</span>
|
||||
<span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> :param max_size: The maximum size of the memory. should be defined in a granularity of Transitions</span>
|
||||
<span class="sd"> :param hindsight_transitions_per_regular_transition: The number of hindsight artificial transitions to generate</span>
|
||||
<span class="sd"> for each actual transition</span>
|
||||
<span class="sd"> :param hindsight_goal_selection_method: The method that will be used for generating the goals for the</span>
|
||||
<span class="sd"> hindsight transitions. Should be one of HindsightGoalSelectionMethod</span>
|
||||
<span class="sd"> :param goals_space: A GoalsSpace which defines the properties of the goals</span>
|
||||
<span class="sd"> :param do_action_hindsight: Replace the action (sub-goal) given to a lower layer, with the actual achieved goal</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">max_size</span><span class="p">,</span> <span class="n">hindsight_transitions_per_regular_transition</span><span class="p">,</span> <span class="n">hindsight_goal_selection_method</span><span class="p">,</span>
|
||||
<span class="n">goals_space</span><span class="p">)</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">store_episode</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">episode</span><span class="p">:</span> <span class="n">Episode</span><span class="p">,</span> <span class="n">lock</span><span class="p">:</span> <span class="nb">bool</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="o">-></span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="c1"># for a layer producing sub-goals, we will replace in hindsight the action (sub-goal) given to the lower</span>
|
||||
<span class="c1"># level with the actual achieved goal. the achieved goal (and observation) seen is assumed to be the same</span>
|
||||
<span class="c1"># for all levels - we can use this level's achieved goal instead of the lower level's one</span>
|
||||
|
||||
<span class="c1"># Calling super.store() so that in case a memory backend is used, the memory backend can store this episode.</span>
|
||||
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">store_episode</span><span class="p">(</span><span class="n">episode</span><span class="p">)</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">transition</span> <span class="ow">in</span> <span class="n">episode</span><span class="o">.</span><span class="n">transitions</span><span class="p">:</span>
|
||||
<span class="n">new_achieved_goal</span> <span class="o">=</span> <span class="n">transition</span><span class="o">.</span><span class="n">next_state</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">goals_space</span><span class="o">.</span><span class="n">goal_name</span><span class="p">]</span>
|
||||
<span class="n">transition</span><span class="o">.</span><span class="n">action</span> <span class="o">=</span> <span class="n">new_achieved_goal</span>
|
||||
|
||||
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">store_episode</span><span class="p">(</span><span class="n">episode</span><span class="p">)</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">store</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">transition</span><span class="p">:</span> <span class="n">Transition</span><span class="p">):</span>
|
||||
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"An episodic HER cannot store a single transition. Only full episodes are to be stored."</span><span class="p">)</span></div>
|
||||
</pre></div>
|
||||
|
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<h1>Source code for rl_coach.memories.episodic.single_episode_buffer</h1><div class="highlight"><pre>
|
||||
<span></span><span class="c1">#</span>
|
||||
<span class="c1"># Copyright (c) 2017 Intel Corporation</span>
|
||||
<span class="c1">#</span>
|
||||
<span class="c1"># Licensed under the Apache License, Version 2.0 (the "License");</span>
|
||||
<span class="c1"># you may not use this file except in compliance with the License.</span>
|
||||
<span class="c1"># You may obtain a copy of the License at</span>
|
||||
<span class="c1">#</span>
|
||||
<span class="c1"># http://www.apache.org/licenses/LICENSE-2.0</span>
|
||||
<span class="c1">#</span>
|
||||
<span class="c1"># Unless required by applicable law or agreed to in writing, software</span>
|
||||
<span class="c1"># distributed under the License is distributed on an "AS IS" BASIS,</span>
|
||||
<span class="c1"># WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
|
||||
<span class="c1"># See the License for the specific language governing permissions and</span>
|
||||
<span class="c1"># limitations under the License.</span>
|
||||
<span class="c1">#</span>
|
||||
|
||||
<span class="kn">from</span> <span class="nn">rl_coach.memories.episodic.episodic_experience_replay</span> <span class="k">import</span> <span class="n">EpisodicExperienceReplay</span>
|
||||
<span class="kn">from</span> <span class="nn">rl_coach.memories.memory</span> <span class="k">import</span> <span class="n">MemoryGranularity</span><span class="p">,</span> <span class="n">MemoryParameters</span>
|
||||
|
||||
|
||||
<span class="k">class</span> <span class="nc">SingleEpisodeBufferParameters</span><span class="p">(</span><span class="n">MemoryParameters</span><span class="p">):</span>
|
||||
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
|
||||
<span class="k">del</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_size</span>
|
||||
|
||||
<span class="nd">@property</span>
|
||||
<span class="k">def</span> <span class="nf">path</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="k">return</span> <span class="s1">'rl_coach.memories.episodic.single_episode_buffer:SingleEpisodeBuffer'</span>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="SingleEpisodeBuffer"><a class="viewcode-back" href="../../../../components/memories/index.html#rl_coach.memories.episodic.SingleEpisodeBuffer">[docs]</a><span class="k">class</span> <span class="nc">SingleEpisodeBuffer</span><span class="p">(</span><span class="n">EpisodicExperienceReplay</span><span class="p">):</span>
|
||||
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">((</span><span class="n">MemoryGranularity</span><span class="o">.</span><span class="n">Episodes</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span></div>
|
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<h1>Source code for rl_coach.memories.non_episodic.balanced_experience_replay</h1><div class="highlight"><pre>
|
||||
<span></span><span class="c1">#</span>
|
||||
<span class="c1"># Copyright (c) 2017 Intel Corporation</span>
|
||||
<span class="c1">#</span>
|
||||
<span class="c1"># Licensed under the Apache License, Version 2.0 (the "License");</span>
|
||||
<span class="c1"># you may not use this file except in compliance with the License.</span>
|
||||
<span class="c1"># You may obtain a copy of the License at</span>
|
||||
<span class="c1">#</span>
|
||||
<span class="c1"># http://www.apache.org/licenses/LICENSE-2.0</span>
|
||||
<span class="c1">#</span>
|
||||
<span class="c1"># Unless required by applicable law or agreed to in writing, software</span>
|
||||
<span class="c1"># distributed under the License is distributed on an "AS IS" BASIS,</span>
|
||||
<span class="c1"># WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
|
||||
<span class="c1"># See the License for the specific language governing permissions and</span>
|
||||
<span class="c1"># limitations under the License.</span>
|
||||
<span class="c1">#</span>
|
||||
|
||||
<span class="kn">import</span> <span class="nn">operator</span>
|
||||
<span class="kn">import</span> <span class="nn">random</span>
|
||||
<span class="kn">from</span> <span class="nn">enum</span> <span class="k">import</span> <span class="n">Enum</span>
|
||||
<span class="kn">from</span> <span class="nn">typing</span> <span class="k">import</span> <span class="n">List</span><span class="p">,</span> <span class="n">Tuple</span><span class="p">,</span> <span class="n">Any</span><span class="p">,</span> <span class="n">Union</span>
|
||||
|
||||
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
|
||||
|
||||
<span class="kn">from</span> <span class="nn">rl_coach.core_types</span> <span class="k">import</span> <span class="n">Transition</span>
|
||||
<span class="kn">from</span> <span class="nn">rl_coach.memories.memory</span> <span class="k">import</span> <span class="n">MemoryGranularity</span>
|
||||
<span class="kn">from</span> <span class="nn">rl_coach.memories.non_episodic.experience_replay</span> <span class="k">import</span> <span class="n">ExperienceReplayParameters</span><span class="p">,</span> <span class="n">ExperienceReplay</span>
|
||||
<span class="kn">from</span> <span class="nn">rl_coach.schedules</span> <span class="k">import</span> <span class="n">Schedule</span><span class="p">,</span> <span class="n">ConstantSchedule</span>
|
||||
|
||||
|
||||
<span class="k">class</span> <span class="nc">BalancedExperienceReplayParameters</span><span class="p">(</span><span class="n">ExperienceReplayParameters</span><span class="p">):</span>
|
||||
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">max_size</span> <span class="o">=</span> <span class="p">(</span><span class="n">MemoryGranularity</span><span class="o">.</span><span class="n">Transitions</span><span class="p">,</span> <span class="mi">1000000</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">allow_duplicates_in_batch_sampling</span> <span class="o">=</span> <span class="kc">False</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">num_classes</span> <span class="o">=</span> <span class="mi">0</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">state_key_with_the_class_index</span> <span class="o">=</span> <span class="s1">'class'</span>
|
||||
|
||||
<span class="nd">@property</span>
|
||||
<span class="k">def</span> <span class="nf">path</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="k">return</span> <span class="s1">'rl_coach.memories.non_episodic.balanced_experience_replay:BalancedExperienceReplay'</span>
|
||||
|
||||
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd">A replay buffer which allows sampling batches which are balanced in terms of the classes that are sampled</span>
|
||||
<span class="sd">"""</span>
|
||||
<div class="viewcode-block" id="BalancedExperienceReplay"><a class="viewcode-back" href="../../../../components/memories/index.html#rl_coach.memories.non_episodic.BalancedExperienceReplay">[docs]</a><span class="k">class</span> <span class="nc">BalancedExperienceReplay</span><span class="p">(</span><span class="n">ExperienceReplay</span><span class="p">):</span>
|
||||
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">max_size</span><span class="p">:</span> <span class="n">Tuple</span><span class="p">[</span><span class="n">MemoryGranularity</span><span class="p">,</span> <span class="nb">int</span><span class="p">],</span> <span class="n">allow_duplicates_in_batch_sampling</span><span class="p">:</span> <span class="nb">bool</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
|
||||
<span class="n">num_classes</span><span class="p">:</span> <span class="nb">int</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">state_key_with_the_class_index</span><span class="p">:</span> <span class="n">Any</span><span class="o">=</span><span class="s1">'class'</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> :param max_size: the maximum number of transitions or episodes to hold in the memory</span>
|
||||
<span class="sd"> :param allow_duplicates_in_batch_sampling: allow having the same transition multiple times in a batch</span>
|
||||
<span class="sd"> :param num_classes: the number of classes in the replayed data</span>
|
||||
<span class="sd"> :param state_key_with_the_class_index: the class index is assumed to be a value in the state dictionary.</span>
|
||||
<span class="sd"> this parameter determines the key to retrieve the class index value</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">max_size</span><span class="p">,</span> <span class="n">allow_duplicates_in_batch_sampling</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">current_class_to_sample_from</span> <span class="o">=</span> <span class="mi">0</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">num_classes</span> <span class="o">=</span> <span class="n">num_classes</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">state_key_with_the_class_index</span> <span class="o">=</span> <span class="n">state_key_with_the_class_index</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">transitions</span> <span class="o">=</span> <span class="p">[[]</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">num_classes</span><span class="p">)]</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">transitions_order</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_classes</span> <span class="o"><</span> <span class="mi">2</span><span class="p">:</span>
|
||||
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"The number of classes for a balanced replay buffer should be at least 2. "</span>
|
||||
<span class="s2">"The number of classes that were defined are: </span><span class="si">{}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">num_classes</span><span class="p">))</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">store</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">transition</span><span class="p">:</span> <span class="n">Transition</span><span class="p">,</span> <span class="n">lock</span><span class="p">:</span> <span class="nb">bool</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="o">-></span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Store a new transition in the memory.</span>
|
||||
<span class="sd"> :param transition: a transition to store</span>
|
||||
<span class="sd"> :param lock: if true, will lock the readers writers lock. this can cause a deadlock if an inheriting class</span>
|
||||
<span class="sd"> locks and then calls store with lock = True</span>
|
||||
<span class="sd"> :return: None</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="c1"># Calling super.store() so that in case a memory backend is used, the memory backend can store this transition.</span>
|
||||
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">store</span><span class="p">(</span><span class="n">transition</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">lock</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">reader_writer_lock</span><span class="o">.</span><span class="n">lock_writing_and_reading</span><span class="p">()</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_num_transitions</span> <span class="o">+=</span> <span class="mi">1</span>
|
||||
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">state_key_with_the_class_index</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">transition</span><span class="o">.</span><span class="n">state</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
|
||||
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"The class index was not present in the state of the transition under the given key (</span><span class="si">{}</span><span class="s2">)"</span>
|
||||
<span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">state_key_with_the_class_index</span><span class="p">))</span>
|
||||
|
||||
<span class="n">class_idx</span> <span class="o">=</span> <span class="n">transition</span><span class="o">.</span><span class="n">state</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">state_key_with_the_class_index</span><span class="p">]</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">class_idx</span> <span class="o">>=</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_classes</span><span class="p">:</span>
|
||||
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"The given class index is outside the defined number of classes for the replay buffer. "</span>
|
||||
<span class="s2">"The given class was: </span><span class="si">{}</span><span class="s2"> and the number of classes defined is: </span><span class="si">{}</span><span class="s2">"</span>
|
||||
<span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">class_idx</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_classes</span><span class="p">))</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">transitions</span><span class="p">[</span><span class="n">class_idx</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">transition</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">transitions_order</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">class_idx</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_enforce_max_length</span><span class="p">()</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">lock</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">reader_writer_lock</span><span class="o">.</span><span class="n">release_writing_and_reading</span><span class="p">()</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">sample</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">size</span><span class="p">:</span> <span class="nb">int</span><span class="p">)</span> <span class="o">-></span> <span class="n">List</span><span class="p">[</span><span class="n">Transition</span><span class="p">]:</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Sample a batch of transitions form the replay buffer. If the requested size is larger than the number</span>
|
||||
<span class="sd"> of samples available in the replay buffer then the batch will return empty.</span>
|
||||
<span class="sd"> :param size: the size of the batch to sample</span>
|
||||
<span class="sd"> :return: a batch (list) of selected transitions from the replay buffer</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">reader_writer_lock</span><span class="o">.</span><span class="n">lock_writing</span><span class="p">()</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">size</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_classes</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
|
||||
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"Sampling batches from a balanced replay buffer should be done only using batch sizes "</span>
|
||||
<span class="s2">"which are a multiple of the number of classes. The number of classes defined is: </span><span class="si">{}</span><span class="s2"> "</span>
|
||||
<span class="s2">"and the batch size requested is: </span><span class="si">{}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">num_classes</span><span class="p">,</span> <span class="n">size</span><span class="p">))</span>
|
||||
|
||||
<span class="n">batch_size_from_each_class</span> <span class="o">=</span> <span class="n">size</span> <span class="o">//</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_classes</span>
|
||||
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">allow_duplicates_in_batch_sampling</span><span class="p">:</span>
|
||||
<span class="n">transitions_idx</span> <span class="o">=</span> <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">class_transitions</span><span class="p">),</span> <span class="n">size</span><span class="o">=</span><span class="n">batch_size_from_each_class</span><span class="p">)</span>
|
||||
<span class="k">for</span> <span class="n">class_transitions</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">transitions</span><span class="p">]</span>
|
||||
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="k">for</span> <span class="n">class_idx</span><span class="p">,</span> <span class="n">class_transitions</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">transitions</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_transitions</span><span class="p">()</span> <span class="o"><</span> <span class="n">batch_size_from_each_class</span><span class="p">:</span>
|
||||
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"The replay buffer cannot be sampled since there are not enough transitions yet. "</span>
|
||||
<span class="s2">"There are currently </span><span class="si">{}</span><span class="s2"> transitions for class </span><span class="si">{}</span><span class="s2">"</span>
|
||||
<span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">class_transitions</span><span class="p">),</span> <span class="n">class_idx</span><span class="p">))</span>
|
||||
|
||||
<span class="n">transitions_idx</span> <span class="o">=</span> <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">class_transitions</span><span class="p">),</span> <span class="n">size</span><span class="o">=</span><span class="n">batch_size_from_each_class</span><span class="p">,</span> <span class="n">replace</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
|
||||
<span class="k">for</span> <span class="n">class_transitions</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">transitions</span><span class="p">]</span>
|
||||
|
||||
<span class="n">batch</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="k">for</span> <span class="n">class_idx</span><span class="p">,</span> <span class="n">class_transitions_idx</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">transitions_idx</span><span class="p">):</span>
|
||||
<span class="n">batch</span> <span class="o">+=</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">transitions</span><span class="p">[</span><span class="n">class_idx</span><span class="p">][</span><span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">class_transitions_idx</span><span class="p">]</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">reader_writer_lock</span><span class="o">.</span><span class="n">release_writing</span><span class="p">()</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">batch</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">remove_transition</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">transition_index</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">lock</span><span class="p">:</span> <span class="nb">bool</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="o">-></span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"It is not possible to remove specific transitions with a balanced replay buffer"</span><span class="p">)</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">get_transition</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">transition_index</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">lock</span><span class="p">:</span> <span class="nb">bool</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="o">-></span> <span class="n">Union</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="n">Transition</span><span class="p">]:</span>
|
||||
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"It is not possible to access specific transitions with a balanced replay buffer"</span><span class="p">)</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">_enforce_max_length</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-></span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Make sure that the size of the replay buffer does not pass the maximum size allowed.</span>
|
||||
<span class="sd"> If it passes the max size, the oldest transition in the replay buffer will be removed.</span>
|
||||
<span class="sd"> This function does not use locks since it is only called internally</span>
|
||||
<span class="sd"> :return: None</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">granularity</span><span class="p">,</span> <span class="n">size</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_size</span>
|
||||
<span class="k">if</span> <span class="n">granularity</span> <span class="o">==</span> <span class="n">MemoryGranularity</span><span class="o">.</span><span class="n">Transitions</span><span class="p">:</span>
|
||||
<span class="k">while</span> <span class="n">size</span> <span class="o">!=</span> <span class="mi">0</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_transitions</span><span class="p">()</span> <span class="o">></span> <span class="n">size</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_num_transitions</span> <span class="o">-=</span> <span class="mi">1</span>
|
||||
<span class="k">del</span> <span class="bp">self</span><span class="o">.</span><span class="n">transitions</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">transitions_order</span><span class="p">[</span><span class="mi">0</span><span class="p">]][</span><span class="mi">0</span><span class="p">]</span>
|
||||
<span class="k">del</span> <span class="bp">self</span><span class="o">.</span><span class="n">transitions_order</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"The granularity of the replay buffer can only be set in terms of transitions"</span><span class="p">)</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">clean</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">lock</span><span class="p">:</span> <span class="nb">bool</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="o">-></span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Clean the memory by removing all the episodes</span>
|
||||
<span class="sd"> :return: None</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">if</span> <span class="n">lock</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">reader_writer_lock</span><span class="o">.</span><span class="n">lock_writing_and_reading</span><span class="p">()</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">transitions</span> <span class="o">=</span> <span class="p">[[]</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">num_classes</span><span class="p">)]</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">transitions_order</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_num_transitions</span> <span class="o">=</span> <span class="mi">0</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">lock</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">reader_writer_lock</span><span class="o">.</span><span class="n">release_writing_and_reading</span><span class="p">()</span></div>
|
||||
</pre></div>
|
||||
|
||||
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<h1>Source code for rl_coach.memories.non_episodic.differentiable_neural_dictionary</h1><div class="highlight"><pre>
|
||||
<span></span><span class="c1">#</span>
|
||||
<span class="c1"># Copyright (c) 2017 Intel Corporation </span>
|
||||
<span class="c1">#</span>
|
||||
<span class="c1"># Licensed under the Apache License, Version 2.0 (the "License");</span>
|
||||
<span class="c1"># you may not use this file except in compliance with the License.</span>
|
||||
<span class="c1"># You may obtain a copy of the License at</span>
|
||||
<span class="c1">#</span>
|
||||
<span class="c1"># http://www.apache.org/licenses/LICENSE-2.0</span>
|
||||
<span class="c1">#</span>
|
||||
<span class="c1"># Unless required by applicable law or agreed to in writing, software</span>
|
||||
<span class="c1"># distributed under the License is distributed on an "AS IS" BASIS,</span>
|
||||
<span class="c1"># WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
|
||||
<span class="c1"># See the License for the specific language governing permissions and</span>
|
||||
<span class="c1"># limitations under the License.</span>
|
||||
<span class="c1">#</span>
|
||||
|
||||
<span class="kn">import</span> <span class="nn">os</span>
|
||||
<span class="kn">import</span> <span class="nn">pickle</span>
|
||||
|
||||
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
|
||||
<span class="k">try</span><span class="p">:</span>
|
||||
<span class="kn">import</span> <span class="nn">annoy</span>
|
||||
<span class="kn">from</span> <span class="nn">annoy</span> <span class="k">import</span> <span class="n">AnnoyIndex</span>
|
||||
<span class="k">except</span> <span class="ne">ImportError</span><span class="p">:</span>
|
||||
<span class="kn">from</span> <span class="nn">rl_coach.logger</span> <span class="k">import</span> <span class="n">failed_imports</span>
|
||||
<span class="n">failed_imports</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s2">"annoy"</span><span class="p">)</span>
|
||||
|
||||
|
||||
<span class="k">class</span> <span class="nc">AnnoyDictionary</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
|
||||
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dict_size</span><span class="p">,</span> <span class="n">key_width</span><span class="p">,</span> <span class="n">new_value_shift_coefficient</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">key_error_threshold</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span>
|
||||
<span class="n">num_neighbors</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">override_existing_keys</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">rebuild_on_every_update</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">rebuild_on_every_update</span> <span class="o">=</span> <span class="n">rebuild_on_every_update</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">max_size</span> <span class="o">=</span> <span class="n">dict_size</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">curr_size</span> <span class="o">=</span> <span class="mi">0</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">new_value_shift_coefficient</span> <span class="o">=</span> <span class="n">new_value_shift_coefficient</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">num_neighbors</span> <span class="o">=</span> <span class="n">num_neighbors</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">override_existing_keys</span> <span class="o">=</span> <span class="n">override_existing_keys</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">index</span> <span class="o">=</span> <span class="n">AnnoyIndex</span><span class="p">(</span><span class="n">key_width</span><span class="p">,</span> <span class="n">metric</span><span class="o">=</span><span class="s1">'euclidean'</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">set_seed</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">embeddings</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">dict_size</span><span class="p">,</span> <span class="n">key_width</span><span class="p">))</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">values</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">dict_size</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">additional_data</span> <span class="o">=</span> <span class="p">[</span><span class="kc">None</span><span class="p">]</span> <span class="o">*</span> <span class="n">dict_size</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">lru_timestamps</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">dict_size</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">current_timestamp</span> <span class="o">=</span> <span class="mf">0.0</span>
|
||||
|
||||
<span class="c1"># keys that are in this distance will be considered as the same key</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">key_error_threshold</span> <span class="o">=</span> <span class="n">key_error_threshold</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">initial_update_size</span> <span class="o">=</span> <span class="n">batch_size</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">min_update_size</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">initial_update_size</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">key_dimension</span> <span class="o">=</span> <span class="n">key_width</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">value_dimension</span> <span class="o">=</span> <span class="mi">1</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_reset_buffer</span><span class="p">()</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">built_capacity</span> <span class="o">=</span> <span class="mi">0</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">add</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">keys</span><span class="p">,</span> <span class="n">values</span><span class="p">,</span> <span class="n">additional_data</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="ow">not</span> <span class="n">additional_data</span><span class="p">:</span>
|
||||
<span class="n">additional_data</span> <span class="o">=</span> <span class="p">[</span><span class="kc">None</span><span class="p">]</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">keys</span><span class="p">)</span>
|
||||
|
||||
<span class="c1"># Adds new embeddings and values to the dictionary</span>
|
||||
<span class="n">indices</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="n">indices_to_remove</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">keys</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]):</span>
|
||||
<span class="n">index</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_lookup_key_index</span><span class="p">(</span><span class="n">keys</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
|
||||
<span class="k">if</span> <span class="n">index</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">override_existing_keys</span><span class="p">:</span>
|
||||
<span class="c1"># update existing value</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">values</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> <span class="o">+=</span> <span class="bp">self</span><span class="o">.</span><span class="n">new_value_shift_coefficient</span> <span class="o">*</span> <span class="p">(</span><span class="n">values</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">values</span><span class="p">[</span><span class="n">index</span><span class="p">])</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">additional_data</span><span class="p">[</span><span class="n">index</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]]</span> <span class="o">=</span> <span class="n">additional_data</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">lru_timestamps</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">current_timestamp</span>
|
||||
<span class="n">indices_to_remove</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="c1"># add new</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">curr_size</span> <span class="o">>=</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_size</span><span class="p">:</span>
|
||||
<span class="c1"># find the LRU entry</span>
|
||||
<span class="n">index</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmin</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">lru_timestamps</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">index</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">curr_size</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">curr_size</span> <span class="o">+=</span> <span class="mi">1</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">lru_timestamps</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">current_timestamp</span>
|
||||
<span class="n">indices</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">index</span><span class="p">)</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">reversed</span><span class="p">(</span><span class="n">indices_to_remove</span><span class="p">):</span>
|
||||
<span class="n">keys</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">delete</span><span class="p">(</span><span class="n">keys</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
|
||||
<span class="n">values</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">delete</span><span class="p">(</span><span class="n">values</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
|
||||
<span class="k">del</span> <span class="n">additional_data</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">buffered_keys</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">((</span><span class="bp">self</span><span class="o">.</span><span class="n">buffered_keys</span><span class="p">,</span> <span class="n">keys</span><span class="p">))</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">buffered_values</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">((</span><span class="bp">self</span><span class="o">.</span><span class="n">buffered_values</span><span class="p">,</span> <span class="n">values</span><span class="p">))</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">buffered_indices</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">buffered_indices</span> <span class="o">+</span> <span class="n">indices</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">buffered_additional_data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">buffered_additional_data</span> <span class="o">+</span> <span class="n">additional_data</span>
|
||||
|
||||
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">buffered_indices</span><span class="p">)</span> <span class="o">>=</span> <span class="bp">self</span><span class="o">.</span><span class="n">min_update_size</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">min_update_size</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">initial_update_size</span><span class="p">,</span> <span class="nb">int</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">curr_size</span> <span class="o">*</span> <span class="mf">0.02</span><span class="p">))</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_rebuild_index</span><span class="p">()</span>
|
||||
<span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">rebuild_on_every_update</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_rebuild_index</span><span class="p">()</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">current_timestamp</span> <span class="o">+=</span> <span class="mi">1</span>
|
||||
|
||||
<span class="c1"># Returns the stored embeddings and values of the closest embeddings</span>
|
||||
<span class="k">def</span> <span class="nf">query</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">keys</span><span class="p">,</span> <span class="n">k</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">has_enough_entries</span><span class="p">(</span><span class="n">k</span><span class="p">):</span>
|
||||
<span class="c1"># this will only happen when the DND is not yet populated with enough entries, which is only during heatup</span>
|
||||
<span class="c1"># these values won't be used and therefore they are meaningless</span>
|
||||
<span class="k">return</span> <span class="p">[</span><span class="mf">0.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">0.0</span><span class="p">],</span> <span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="p">[</span><span class="kc">None</span><span class="p">]</span>
|
||||
|
||||
<span class="n">_</span><span class="p">,</span> <span class="n">indices</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_k_nearest_neighbors_indices</span><span class="p">(</span><span class="n">keys</span><span class="p">,</span> <span class="n">k</span><span class="p">)</span>
|
||||
|
||||
<span class="n">embeddings</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="n">values</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="n">additional_data</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="k">for</span> <span class="n">ind</span> <span class="ow">in</span> <span class="n">indices</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">lru_timestamps</span><span class="p">[</span><span class="n">ind</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">current_timestamp</span>
|
||||
<span class="n">embeddings</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">embeddings</span><span class="p">[</span><span class="n">ind</span><span class="p">])</span>
|
||||
<span class="n">values</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">values</span><span class="p">[</span><span class="n">ind</span><span class="p">])</span>
|
||||
<span class="n">curr_additional_data</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="k">for</span> <span class="n">sub_ind</span> <span class="ow">in</span> <span class="n">ind</span><span class="p">:</span>
|
||||
<span class="n">curr_additional_data</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">additional_data</span><span class="p">[</span><span class="n">sub_ind</span><span class="p">])</span>
|
||||
<span class="n">additional_data</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">curr_additional_data</span><span class="p">)</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">current_timestamp</span> <span class="o">+=</span> <span class="mi">1</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">embeddings</span><span class="p">,</span> <span class="n">values</span><span class="p">,</span> <span class="n">indices</span><span class="p">,</span> <span class="n">additional_data</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">has_enough_entries</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">k</span><span class="p">):</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">curr_size</span> <span class="o">></span> <span class="n">k</span> <span class="ow">and</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">built_capacity</span> <span class="o">></span> <span class="n">k</span><span class="p">)</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">sample_embeddings</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">num_embeddings</span><span class="p">):</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">embeddings</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">curr_size</span><span class="p">,</span> <span class="n">num_embeddings</span><span class="p">)]</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">_get_k_nearest_neighbors_indices</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">keys</span><span class="p">,</span> <span class="n">k</span><span class="p">):</span>
|
||||
<span class="n">distances</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="n">indices</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="n">keys</span><span class="p">:</span>
|
||||
<span class="n">index</span><span class="p">,</span> <span class="n">distance</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">get_nns_by_vector</span><span class="p">(</span><span class="n">key</span><span class="p">,</span> <span class="n">k</span><span class="p">,</span> <span class="n">include_distances</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
|
||||
<span class="n">distances</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">distance</span><span class="p">)</span>
|
||||
<span class="n">indices</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">index</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="n">distances</span><span class="p">,</span> <span class="n">indices</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">_rebuild_index</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">unbuild</span><span class="p">()</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">embeddings</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">buffered_indices</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">buffered_keys</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">values</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">buffered_indices</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">squeeze</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">buffered_values</span><span class="p">)</span>
|
||||
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">data</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">buffered_indices</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">buffered_additional_data</span><span class="p">):</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">additional_data</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">data</span>
|
||||
<span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="n">key</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">buffered_indices</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">buffered_keys</span><span class="p">):</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">add_item</span><span class="p">(</span><span class="n">idx</span><span class="p">,</span> <span class="n">key</span><span class="p">)</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_reset_buffer</span><span class="p">()</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">build</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">num_neighbors</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">built_capacity</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">curr_size</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">_reset_buffer</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">buffered_keys</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">key_dimension</span><span class="p">))</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">buffered_values</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">value_dimension</span><span class="p">))</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">buffered_indices</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">buffered_additional_data</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">_lookup_key_index</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">key</span><span class="p">):</span>
|
||||
<span class="n">distance</span><span class="p">,</span> <span class="n">index</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_k_nearest_neighbors_indices</span><span class="p">([</span><span class="n">key</span><span class="p">],</span> <span class="mi">1</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">distance</span> <span class="o">!=</span> <span class="p">[[]]</span> <span class="ow">and</span> <span class="n">distance</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o"><=</span> <span class="bp">self</span><span class="o">.</span><span class="n">key_error_threshold</span><span class="p">:</span>
|
||||
<span class="k">return</span> <span class="n">index</span>
|
||||
<span class="k">return</span> <span class="kc">None</span>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="QDND"><a class="viewcode-back" href="../../../../components/memories/index.html#rl_coach.memories.non_episodic.QDND">[docs]</a><span class="k">class</span> <span class="nc">QDND</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
|
||||
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">dict_size</span><span class="p">,</span> <span class="n">key_width</span><span class="p">,</span> <span class="n">num_actions</span><span class="p">,</span> <span class="n">new_value_shift_coefficient</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">key_error_threshold</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span>
|
||||
<span class="n">learning_rate</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">num_neighbors</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">return_additional_data</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">override_existing_keys</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
|
||||
<span class="n">rebuild_on_every_update</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">dict_size</span> <span class="o">=</span> <span class="n">dict_size</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">key_width</span> <span class="o">=</span> <span class="n">key_width</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">num_actions</span> <span class="o">=</span> <span class="n">num_actions</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">new_value_shift_coefficient</span> <span class="o">=</span> <span class="n">new_value_shift_coefficient</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">key_error_threshold</span> <span class="o">=</span> <span class="n">key_error_threshold</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">learning_rate</span> <span class="o">=</span> <span class="n">learning_rate</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">num_neighbors</span> <span class="o">=</span> <span class="n">num_neighbors</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">return_additional_data</span> <span class="o">=</span> <span class="n">return_additional_data</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">override_existing_keys</span> <span class="o">=</span> <span class="n">override_existing_keys</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">dicts</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
|
||||
<span class="c1"># create a dict for each action</span>
|
||||
<span class="k">for</span> <span class="n">a</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_actions</span><span class="p">):</span>
|
||||
<span class="n">new_dict</span> <span class="o">=</span> <span class="n">AnnoyDictionary</span><span class="p">(</span><span class="n">dict_size</span><span class="p">,</span> <span class="n">key_width</span><span class="p">,</span> <span class="n">new_value_shift_coefficient</span><span class="p">,</span>
|
||||
<span class="n">key_error_threshold</span><span class="o">=</span><span class="n">key_error_threshold</span><span class="p">,</span> <span class="n">num_neighbors</span><span class="o">=</span><span class="n">num_neighbors</span><span class="p">,</span>
|
||||
<span class="n">override_existing_keys</span><span class="o">=</span><span class="n">override_existing_keys</span><span class="p">,</span>
|
||||
<span class="n">rebuild_on_every_update</span><span class="o">=</span><span class="n">rebuild_on_every_update</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">dicts</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">new_dict</span><span class="p">)</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">add</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">embeddings</span><span class="p">,</span> <span class="n">actions</span><span class="p">,</span> <span class="n">values</span><span class="p">,</span> <span class="n">additional_data</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
|
||||
<span class="c1"># add a new set of embeddings and values to each of the underlining dictionaries</span>
|
||||
<span class="n">embeddings</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">embeddings</span><span class="p">)</span>
|
||||
<span class="n">actions</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">actions</span><span class="p">)</span>
|
||||
<span class="n">values</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">values</span><span class="p">)</span>
|
||||
<span class="k">for</span> <span class="n">a</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">num_actions</span><span class="p">):</span>
|
||||
<span class="n">idx</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">actions</span> <span class="o">==</span> <span class="n">a</span><span class="p">)</span>
|
||||
<span class="n">curr_action_embeddings</span> <span class="o">=</span> <span class="n">embeddings</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span>
|
||||
<span class="n">curr_action_values</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">expand_dims</span><span class="p">(</span><span class="n">values</span><span class="p">[</span><span class="n">idx</span><span class="p">],</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">additional_data</span><span class="p">:</span>
|
||||
<span class="n">curr_additional_data</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">idx</span><span class="p">[</span><span class="mi">0</span><span class="p">]:</span>
|
||||
<span class="n">curr_additional_data</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">additional_data</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">curr_additional_data</span> <span class="o">=</span> <span class="kc">None</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">dicts</span><span class="p">[</span><span class="n">a</span><span class="p">]</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">curr_action_embeddings</span><span class="p">,</span> <span class="n">curr_action_values</span><span class="p">,</span> <span class="n">curr_additional_data</span><span class="p">)</span>
|
||||
<span class="k">return</span> <span class="kc">True</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">query</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">embeddings</span><span class="p">,</span> <span class="n">action</span><span class="p">,</span> <span class="n">k</span><span class="p">):</span>
|
||||
<span class="c1"># query for nearest neighbors to the given embeddings</span>
|
||||
<span class="n">dnd_embeddings</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="n">dnd_values</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="n">dnd_indices</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="n">dnd_additional_data</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">embeddings</span><span class="p">)):</span>
|
||||
<span class="n">embedding</span><span class="p">,</span> <span class="n">value</span><span class="p">,</span> <span class="n">indices</span><span class="p">,</span> <span class="n">additional_data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dicts</span><span class="p">[</span><span class="n">action</span><span class="p">]</span><span class="o">.</span><span class="n">query</span><span class="p">([</span><span class="n">embeddings</span><span class="p">[</span><span class="n">i</span><span class="p">]],</span> <span class="n">k</span><span class="p">)</span>
|
||||
<span class="n">dnd_embeddings</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">embedding</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
|
||||
<span class="n">dnd_values</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">value</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
|
||||
<span class="n">dnd_indices</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">indices</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
|
||||
<span class="n">dnd_additional_data</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">additional_data</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
|
||||
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">return_additional_data</span><span class="p">:</span>
|
||||
<span class="k">return</span> <span class="n">dnd_embeddings</span><span class="p">,</span> <span class="n">dnd_values</span><span class="p">,</span> <span class="n">dnd_indices</span><span class="p">,</span> <span class="n">dnd_additional_data</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="k">return</span> <span class="n">dnd_embeddings</span><span class="p">,</span> <span class="n">dnd_values</span><span class="p">,</span> <span class="n">dnd_indices</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">has_enough_entries</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">k</span><span class="p">):</span>
|
||||
<span class="c1"># check if each of the action dictionaries has at least k entries</span>
|
||||
<span class="k">for</span> <span class="n">a</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">num_actions</span><span class="p">):</span>
|
||||
<span class="k">if</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">dicts</span><span class="p">[</span><span class="n">a</span><span class="p">]</span><span class="o">.</span><span class="n">has_enough_entries</span><span class="p">(</span><span class="n">k</span><span class="p">):</span>
|
||||
<span class="k">return</span> <span class="kc">False</span>
|
||||
<span class="k">return</span> <span class="kc">True</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">update_keys_and_values</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">actions</span><span class="p">,</span> <span class="n">key_gradients</span><span class="p">,</span> <span class="n">value_gradients</span><span class="p">,</span> <span class="n">indices</span><span class="p">):</span>
|
||||
<span class="c1"># Update DND keys and values</span>
|
||||
<span class="k">for</span> <span class="n">batch_action</span><span class="p">,</span> <span class="n">batch_keys</span><span class="p">,</span> <span class="n">batch_values</span><span class="p">,</span> <span class="n">batch_indices</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">actions</span><span class="p">,</span> <span class="n">key_gradients</span><span class="p">,</span> <span class="n">value_gradients</span><span class="p">,</span> <span class="n">indices</span><span class="p">):</span>
|
||||
<span class="c1"># Update keys (embeddings) and values in DND</span>
|
||||
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">index</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">batch_indices</span><span class="p">):</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">dicts</span><span class="p">[</span><span class="n">batch_action</span><span class="p">]</span><span class="o">.</span><span class="n">embeddings</span><span class="p">[</span><span class="n">index</span><span class="p">,</span> <span class="p">:]</span> <span class="o">-=</span> <span class="bp">self</span><span class="o">.</span><span class="n">learning_rate</span> <span class="o">*</span> <span class="n">batch_keys</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="p">:]</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">dicts</span><span class="p">[</span><span class="n">batch_action</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">[</span><span class="n">index</span><span class="p">]</span> <span class="o">-=</span> <span class="bp">self</span><span class="o">.</span><span class="n">learning_rate</span> <span class="o">*</span> <span class="n">batch_values</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">sample_embeddings</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">num_embeddings</span><span class="p">):</span>
|
||||
<span class="n">num_actions</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dicts</span><span class="p">)</span>
|
||||
<span class="n">embeddings</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="n">num_embeddings_per_action</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">num_embeddings</span><span class="o">/</span><span class="n">num_actions</span><span class="p">)</span>
|
||||
<span class="k">for</span> <span class="n">action</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_actions</span><span class="p">):</span>
|
||||
<span class="n">embeddings</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dicts</span><span class="p">[</span><span class="n">action</span><span class="p">]</span><span class="o">.</span><span class="n">sample_embeddings</span><span class="p">(</span><span class="n">num_embeddings_per_action</span><span class="p">))</span>
|
||||
<span class="n">embeddings</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">(</span><span class="n">embeddings</span><span class="p">)</span>
|
||||
|
||||
<span class="c1"># the numbers did not divide nicely, let's just randomly sample some more embeddings</span>
|
||||
<span class="k">if</span> <span class="n">num_embeddings_per_action</span> <span class="o">*</span> <span class="n">num_actions</span> <span class="o"><</span> <span class="n">num_embeddings</span><span class="p">:</span>
|
||||
<span class="n">action</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">num_actions</span><span class="p">)</span>
|
||||
<span class="n">extra_embeddings</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">dicts</span><span class="p">[</span><span class="n">action</span><span class="p">]</span><span class="o">.</span><span class="n">sample_embeddings</span><span class="p">(</span><span class="n">num_embeddings</span> <span class="o">-</span>
|
||||
<span class="n">num_embeddings_per_action</span> <span class="o">*</span> <span class="n">num_actions</span><span class="p">)</span>
|
||||
<span class="n">embeddings</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">([</span><span class="n">embeddings</span><span class="p">,</span> <span class="n">extra_embeddings</span><span class="p">])</span>
|
||||
<span class="k">return</span> <span class="n">embeddings</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">clean</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="c1"># create a new dict for each action</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">dicts</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="k">for</span> <span class="n">a</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">num_actions</span><span class="p">):</span>
|
||||
<span class="n">new_dict</span> <span class="o">=</span> <span class="n">AnnoyDictionary</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">dict_size</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">key_width</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">new_value_shift_coefficient</span><span class="p">,</span>
|
||||
<span class="n">key_error_threshold</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">key_error_threshold</span><span class="p">,</span> <span class="n">num_neighbors</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">num_neighbors</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">dicts</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">new_dict</span><span class="p">)</span></div>
|
||||
|
||||
|
||||
<span class="k">def</span> <span class="nf">load_dnd</span><span class="p">(</span><span class="n">model_dir</span><span class="p">):</span>
|
||||
<span class="n">max_id</span> <span class="o">=</span> <span class="mi">0</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">f</span> <span class="ow">in</span> <span class="p">[</span><span class="n">s</span> <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">os</span><span class="o">.</span><span class="n">listdir</span><span class="p">(</span><span class="n">model_dir</span><span class="p">)</span> <span class="k">if</span> <span class="n">s</span><span class="o">.</span><span class="n">endswith</span><span class="p">(</span><span class="s1">'.dnd'</span><span class="p">)]:</span>
|
||||
<span class="k">if</span> <span class="nb">int</span><span class="p">(</span><span class="n">f</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">'.'</span><span class="p">)[</span><span class="mi">0</span><span class="p">])</span> <span class="o">></span> <span class="n">max_id</span><span class="p">:</span>
|
||||
<span class="n">max_id</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">f</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">'.'</span><span class="p">)[</span><span class="mi">0</span><span class="p">])</span>
|
||||
|
||||
<span class="n">model_path</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">max_id</span><span class="p">)</span> <span class="o">+</span> <span class="s1">'.dnd'</span>
|
||||
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">model_dir</span><span class="p">,</span> <span class="n">model_path</span><span class="p">),</span> <span class="s1">'rb'</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
|
||||
<span class="n">DND</span> <span class="o">=</span> <span class="n">pickle</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">f</span><span class="p">)</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">a</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">DND</span><span class="o">.</span><span class="n">num_actions</span><span class="p">):</span>
|
||||
<span class="n">DND</span><span class="o">.</span><span class="n">dicts</span><span class="p">[</span><span class="n">a</span><span class="p">]</span><span class="o">.</span><span class="n">index</span> <span class="o">=</span> <span class="n">AnnoyIndex</span><span class="p">(</span><span class="mi">512</span><span class="p">,</span> <span class="n">metric</span><span class="o">=</span><span class="s1">'euclidean'</span><span class="p">)</span>
|
||||
<span class="n">DND</span><span class="o">.</span><span class="n">dicts</span><span class="p">[</span><span class="n">a</span><span class="p">]</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">set_seed</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
|
||||
|
||||
<span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="n">key</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">DND</span><span class="o">.</span><span class="n">dicts</span><span class="p">[</span><span class="n">a</span><span class="p">]</span><span class="o">.</span><span class="n">curr_size</span><span class="p">),</span> <span class="n">DND</span><span class="o">.</span><span class="n">dicts</span><span class="p">[</span><span class="n">a</span><span class="p">]</span><span class="o">.</span><span class="n">embeddings</span><span class="p">[:</span><span class="n">DND</span><span class="o">.</span><span class="n">dicts</span><span class="p">[</span><span class="n">a</span><span class="p">]</span><span class="o">.</span><span class="n">curr_size</span><span class="p">]):</span>
|
||||
<span class="n">DND</span><span class="o">.</span><span class="n">dicts</span><span class="p">[</span><span class="n">a</span><span class="p">]</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">add_item</span><span class="p">(</span><span class="n">idx</span><span class="p">,</span> <span class="n">key</span><span class="p">)</span>
|
||||
|
||||
<span class="n">DND</span><span class="o">.</span><span class="n">dicts</span><span class="p">[</span><span class="n">a</span><span class="p">]</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">build</span><span class="p">(</span><span class="mi">50</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">DND</span>
|
||||
</pre></div>
|
||||
|
||||
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|
||||
|
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<li><a href="../../../../index.html">Docs</a> »</li>
|
||||
|
||||
<li><a href="../../../index.html">Module code</a> »</li>
|
||||
|
||||
<li>rl_coach.memories.non_episodic.experience_replay</li>
|
||||
|
||||
|
||||
<li class="wy-breadcrumbs-aside">
|
||||
|
||||
</li>
|
||||
|
||||
</ul>
|
||||
|
||||
|
||||
<hr/>
|
||||
</div>
|
||||
<div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
|
||||
<div itemprop="articleBody">
|
||||
|
||||
<h1>Source code for rl_coach.memories.non_episodic.experience_replay</h1><div class="highlight"><pre>
|
||||
<span></span><span class="c1">#</span>
|
||||
<span class="c1"># Copyright (c) 2017 Intel Corporation</span>
|
||||
<span class="c1">#</span>
|
||||
<span class="c1"># Licensed under the Apache License, Version 2.0 (the "License");</span>
|
||||
<span class="c1"># you may not use this file except in compliance with the License.</span>
|
||||
<span class="c1"># You may obtain a copy of the License at</span>
|
||||
<span class="c1">#</span>
|
||||
<span class="c1"># http://www.apache.org/licenses/LICENSE-2.0</span>
|
||||
<span class="c1">#</span>
|
||||
<span class="c1"># Unless required by applicable law or agreed to in writing, software</span>
|
||||
<span class="c1"># distributed under the License is distributed on an "AS IS" BASIS,</span>
|
||||
<span class="c1"># WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
|
||||
<span class="c1"># See the License for the specific language governing permissions and</span>
|
||||
<span class="c1"># limitations under the License.</span>
|
||||
<span class="c1">#</span>
|
||||
|
||||
<span class="kn">from</span> <span class="nn">typing</span> <span class="k">import</span> <span class="n">List</span><span class="p">,</span> <span class="n">Tuple</span><span class="p">,</span> <span class="n">Union</span><span class="p">,</span> <span class="n">Dict</span><span class="p">,</span> <span class="n">Any</span>
|
||||
<span class="kn">import</span> <span class="nn">pickle</span>
|
||||
<span class="kn">import</span> <span class="nn">sys</span>
|
||||
<span class="kn">import</span> <span class="nn">time</span>
|
||||
|
||||
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
|
||||
|
||||
<span class="kn">from</span> <span class="nn">rl_coach.core_types</span> <span class="k">import</span> <span class="n">Transition</span>
|
||||
<span class="kn">from</span> <span class="nn">rl_coach.logger</span> <span class="k">import</span> <span class="n">screen</span>
|
||||
<span class="kn">from</span> <span class="nn">rl_coach.memories.memory</span> <span class="k">import</span> <span class="n">Memory</span><span class="p">,</span> <span class="n">MemoryGranularity</span><span class="p">,</span> <span class="n">MemoryParameters</span>
|
||||
<span class="kn">from</span> <span class="nn">rl_coach.utils</span> <span class="k">import</span> <span class="n">ReaderWriterLock</span><span class="p">,</span> <span class="n">ProgressBar</span>
|
||||
|
||||
|
||||
<span class="k">class</span> <span class="nc">ExperienceReplayParameters</span><span class="p">(</span><span class="n">MemoryParameters</span><span class="p">):</span>
|
||||
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">max_size</span> <span class="o">=</span> <span class="p">(</span><span class="n">MemoryGranularity</span><span class="o">.</span><span class="n">Transitions</span><span class="p">,</span> <span class="mi">1000000</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">allow_duplicates_in_batch_sampling</span> <span class="o">=</span> <span class="kc">True</span>
|
||||
|
||||
<span class="nd">@property</span>
|
||||
<span class="k">def</span> <span class="nf">path</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="k">return</span> <span class="s1">'rl_coach.memories.non_episodic.experience_replay:ExperienceReplay'</span>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="ExperienceReplay"><a class="viewcode-back" href="../../../../components/memories/index.html#rl_coach.memories.non_episodic.ExperienceReplay">[docs]</a><span class="k">class</span> <span class="nc">ExperienceReplay</span><span class="p">(</span><span class="n">Memory</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> A regular replay buffer which stores transition without any additional structure</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">max_size</span><span class="p">:</span> <span class="n">Tuple</span><span class="p">[</span><span class="n">MemoryGranularity</span><span class="p">,</span> <span class="nb">int</span><span class="p">],</span> <span class="n">allow_duplicates_in_batch_sampling</span><span class="p">:</span> <span class="nb">bool</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> :param max_size: the maximum number of transitions or episodes to hold in the memory</span>
|
||||
<span class="sd"> :param allow_duplicates_in_batch_sampling: allow having the same transition multiple times in a batch</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">max_size</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">max_size</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">!=</span> <span class="n">MemoryGranularity</span><span class="o">.</span><span class="n">Transitions</span><span class="p">:</span>
|
||||
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"Experience replay size can only be configured in terms of transitions"</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">transitions</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">allow_duplicates_in_batch_sampling</span> <span class="o">=</span> <span class="n">allow_duplicates_in_batch_sampling</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">reader_writer_lock</span> <span class="o">=</span> <span class="n">ReaderWriterLock</span><span class="p">()</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">length</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-></span> <span class="nb">int</span><span class="p">:</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Get the number of transitions in the ER</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_transitions</span><span class="p">()</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">num_transitions</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-></span> <span class="nb">int</span><span class="p">:</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Get the number of transitions in the ER</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">transitions</span><span class="p">)</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">sample</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">size</span><span class="p">:</span> <span class="nb">int</span><span class="p">)</span> <span class="o">-></span> <span class="n">List</span><span class="p">[</span><span class="n">Transition</span><span class="p">]:</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Sample a batch of transitions form the replay buffer. If the requested size is larger than the number</span>
|
||||
<span class="sd"> of samples available in the replay buffer then the batch will return empty.</span>
|
||||
<span class="sd"> :param size: the size of the batch to sample</span>
|
||||
<span class="sd"> :param beta: the beta parameter used for importance sampling</span>
|
||||
<span class="sd"> :return: a batch (list) of selected transitions from the replay buffer</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">reader_writer_lock</span><span class="o">.</span><span class="n">lock_writing</span><span class="p">()</span>
|
||||
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">allow_duplicates_in_batch_sampling</span><span class="p">:</span>
|
||||
<span class="n">transitions_idx</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">num_transitions</span><span class="p">(),</span> <span class="n">size</span><span class="o">=</span><span class="n">size</span><span class="p">)</span>
|
||||
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_transitions</span><span class="p">()</span> <span class="o">>=</span> <span class="n">size</span><span class="p">:</span>
|
||||
<span class="n">transitions_idx</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">choice</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">num_transitions</span><span class="p">(),</span> <span class="n">size</span><span class="o">=</span><span class="n">size</span><span class="p">,</span> <span class="n">replace</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"The replay buffer cannot be sampled since there are not enough transitions yet. "</span>
|
||||
<span class="s2">"There are currently </span><span class="si">{}</span><span class="s2"> transitions"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">num_transitions</span><span class="p">()))</span>
|
||||
|
||||
<span class="n">batch</span> <span class="o">=</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">transitions</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">transitions_idx</span><span class="p">]</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">reader_writer_lock</span><span class="o">.</span><span class="n">release_writing</span><span class="p">()</span>
|
||||
<span class="k">return</span> <span class="n">batch</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">_enforce_max_length</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-></span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Make sure that the size of the replay buffer does not pass the maximum size allowed.</span>
|
||||
<span class="sd"> If it passes the max size, the oldest transition in the replay buffer will be removed.</span>
|
||||
<span class="sd"> This function does not use locks since it is only called internally</span>
|
||||
<span class="sd"> :return: None</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">granularity</span><span class="p">,</span> <span class="n">size</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_size</span>
|
||||
<span class="k">if</span> <span class="n">granularity</span> <span class="o">==</span> <span class="n">MemoryGranularity</span><span class="o">.</span><span class="n">Transitions</span><span class="p">:</span>
|
||||
<span class="k">while</span> <span class="n">size</span> <span class="o">!=</span> <span class="mi">0</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_transitions</span><span class="p">()</span> <span class="o">></span> <span class="n">size</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">remove_transition</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"The granularity of the replay buffer can only be set in terms of transitions"</span><span class="p">)</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">store</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">transition</span><span class="p">:</span> <span class="n">Transition</span><span class="p">,</span> <span class="n">lock</span><span class="p">:</span> <span class="nb">bool</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="o">-></span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Store a new transition in the memory.</span>
|
||||
<span class="sd"> :param transition: a transition to store</span>
|
||||
<span class="sd"> :param lock: if true, will lock the readers writers lock. this can cause a deadlock if an inheriting class</span>
|
||||
<span class="sd"> locks and then calls store with lock = True</span>
|
||||
<span class="sd"> :return: None</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="c1"># Calling super.store() so that in case a memory backend is used, the memory backend can store this transition.</span>
|
||||
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">store</span><span class="p">(</span><span class="n">transition</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">lock</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">reader_writer_lock</span><span class="o">.</span><span class="n">lock_writing_and_reading</span><span class="p">()</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">transitions</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">transition</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_enforce_max_length</span><span class="p">()</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">lock</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">reader_writer_lock</span><span class="o">.</span><span class="n">release_writing_and_reading</span><span class="p">()</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">get_transition</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">transition_index</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">lock</span><span class="p">:</span> <span class="nb">bool</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="o">-></span> <span class="n">Union</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="n">Transition</span><span class="p">]:</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Returns the transition in the given index. If the transition does not exist, returns None instead.</span>
|
||||
<span class="sd"> :param transition_index: the index of the transition to return</span>
|
||||
<span class="sd"> :param lock: use write locking if this is a shared memory</span>
|
||||
<span class="sd"> :return: the corresponding transition</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">if</span> <span class="n">lock</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">reader_writer_lock</span><span class="o">.</span><span class="n">lock_writing</span><span class="p">()</span>
|
||||
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">length</span><span class="p">()</span> <span class="o">==</span> <span class="mi">0</span> <span class="ow">or</span> <span class="n">transition_index</span> <span class="o">>=</span> <span class="bp">self</span><span class="o">.</span><span class="n">length</span><span class="p">():</span>
|
||||
<span class="n">transition</span> <span class="o">=</span> <span class="kc">None</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">transition</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">transitions</span><span class="p">[</span><span class="n">transition_index</span><span class="p">]</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">lock</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">reader_writer_lock</span><span class="o">.</span><span class="n">release_writing</span><span class="p">()</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">transition</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">remove_transition</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">transition_index</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">lock</span><span class="p">:</span> <span class="nb">bool</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="o">-></span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Remove the transition in the given index.</span>
|
||||
|
||||
<span class="sd"> This does not remove the transition from the segment trees! it is just used to remove the transition</span>
|
||||
<span class="sd"> from the transitions list</span>
|
||||
<span class="sd"> :param transition_index: the index of the transition to remove</span>
|
||||
<span class="sd"> :return: None</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">if</span> <span class="n">lock</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">reader_writer_lock</span><span class="o">.</span><span class="n">lock_writing_and_reading</span><span class="p">()</span>
|
||||
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_transitions</span><span class="p">()</span> <span class="o">></span> <span class="n">transition_index</span><span class="p">:</span>
|
||||
<span class="k">del</span> <span class="bp">self</span><span class="o">.</span><span class="n">transitions</span><span class="p">[</span><span class="n">transition_index</span><span class="p">]</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">lock</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">reader_writer_lock</span><span class="o">.</span><span class="n">release_writing_and_reading</span><span class="p">()</span>
|
||||
|
||||
<span class="c1"># for API compatibility</span>
|
||||
<span class="k">def</span> <span class="nf">get</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">transition_index</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">lock</span><span class="p">:</span> <span class="nb">bool</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="o">-></span> <span class="n">Union</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="n">Transition</span><span class="p">]:</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Returns the transition in the given index. If the transition does not exist, returns None instead.</span>
|
||||
<span class="sd"> :param transition_index: the index of the transition to return</span>
|
||||
<span class="sd"> :return: the corresponding transition</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_transition</span><span class="p">(</span><span class="n">transition_index</span><span class="p">,</span> <span class="n">lock</span><span class="p">)</span>
|
||||
|
||||
<span class="c1"># for API compatibility</span>
|
||||
<span class="k">def</span> <span class="nf">remove</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">transition_index</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">lock</span><span class="p">:</span> <span class="nb">bool</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Remove the transition in the given index</span>
|
||||
<span class="sd"> :param transition_index: the index of the transition to remove</span>
|
||||
<span class="sd"> :return: None</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">remove_transition</span><span class="p">(</span><span class="n">transition_index</span><span class="p">,</span> <span class="n">lock</span><span class="p">)</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">clean</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">lock</span><span class="p">:</span> <span class="nb">bool</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="o">-></span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Clean the memory by removing all the episodes</span>
|
||||
<span class="sd"> :return: None</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">if</span> <span class="n">lock</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">reader_writer_lock</span><span class="o">.</span><span class="n">lock_writing_and_reading</span><span class="p">()</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">transitions</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">lock</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">reader_writer_lock</span><span class="o">.</span><span class="n">release_writing_and_reading</span><span class="p">()</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">mean_reward</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-></span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">:</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Get the mean reward in the replay buffer</span>
|
||||
<span class="sd"> :return: the mean reward</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">reader_writer_lock</span><span class="o">.</span><span class="n">lock_writing</span><span class="p">()</span>
|
||||
|
||||
<span class="n">mean</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">([</span><span class="n">transition</span><span class="o">.</span><span class="n">reward</span> <span class="k">for</span> <span class="n">transition</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">transitions</span><span class="p">])</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">reader_writer_lock</span><span class="o">.</span><span class="n">release_writing</span><span class="p">()</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">mean</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">save</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">file_path</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-></span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Save the replay buffer contents to a pickle file</span>
|
||||
<span class="sd"> :param file_path: the path to the file that will be used to store the pickled transitions</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">file_path</span><span class="p">,</span> <span class="s1">'wb'</span><span class="p">)</span> <span class="k">as</span> <span class="n">file</span><span class="p">:</span>
|
||||
<span class="n">pickle</span><span class="o">.</span><span class="n">dump</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">transitions</span><span class="p">,</span> <span class="n">file</span><span class="p">)</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">load</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">file_path</span><span class="p">:</span> <span class="nb">str</span><span class="p">)</span> <span class="o">-></span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Restore the replay buffer contents from a pickle file.</span>
|
||||
<span class="sd"> The pickle file is assumed to include a list of transitions.</span>
|
||||
<span class="sd"> :param file_path: The path to a pickle file to restore</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">file_path</span><span class="p">,</span> <span class="s1">'rb'</span><span class="p">)</span> <span class="k">as</span> <span class="n">file</span><span class="p">:</span>
|
||||
<span class="n">transitions</span> <span class="o">=</span> <span class="n">pickle</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">file</span><span class="p">)</span>
|
||||
<span class="n">num_transitions</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">transitions</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">num_transitions</span> <span class="o">></span> <span class="bp">self</span><span class="o">.</span><span class="n">max_size</span><span class="p">[</span><span class="mi">1</span><span class="p">]:</span>
|
||||
<span class="n">screen</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span><span class="s2">"Warning! The number of transition to load into the replay buffer (</span><span class="si">{}</span><span class="s2">) is "</span>
|
||||
<span class="s2">"bigger than the max size of the replay buffer (</span><span class="si">{}</span><span class="s2">). The excessive transitions will "</span>
|
||||
<span class="s2">"not be stored."</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">num_transitions</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_size</span><span class="p">[</span><span class="mi">1</span><span class="p">]))</span>
|
||||
|
||||
<span class="n">progress_bar</span> <span class="o">=</span> <span class="n">ProgressBar</span><span class="p">(</span><span class="n">num_transitions</span><span class="p">)</span>
|
||||
<span class="k">for</span> <span class="n">transition_idx</span><span class="p">,</span> <span class="n">transition</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">transitions</span><span class="p">):</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">store</span><span class="p">(</span><span class="n">transition</span><span class="p">)</span>
|
||||
|
||||
<span class="c1"># print progress</span>
|
||||
<span class="k">if</span> <span class="n">transition_idx</span> <span class="o">%</span> <span class="mi">100</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
|
||||
<span class="n">progress_bar</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">transition_idx</span><span class="p">)</span>
|
||||
|
||||
<span class="n">progress_bar</span><span class="o">.</span><span class="n">close</span><span class="p">()</span></div>
|
||||
</pre></div>
|
||||
|
||||
</div>
|
||||
|
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<h1>Source code for rl_coach.memories.non_episodic.prioritized_experience_replay</h1><div class="highlight"><pre>
|
||||
<span></span><span class="c1">#</span>
|
||||
<span class="c1"># Copyright (c) 2017 Intel Corporation</span>
|
||||
<span class="c1">#</span>
|
||||
<span class="c1"># Licensed under the Apache License, Version 2.0 (the "License");</span>
|
||||
<span class="c1"># you may not use this file except in compliance with the License.</span>
|
||||
<span class="c1"># You may obtain a copy of the License at</span>
|
||||
<span class="c1">#</span>
|
||||
<span class="c1"># http://www.apache.org/licenses/LICENSE-2.0</span>
|
||||
<span class="c1">#</span>
|
||||
<span class="c1"># Unless required by applicable law or agreed to in writing, software</span>
|
||||
<span class="c1"># distributed under the License is distributed on an "AS IS" BASIS,</span>
|
||||
<span class="c1"># WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
|
||||
<span class="c1"># See the License for the specific language governing permissions and</span>
|
||||
<span class="c1"># limitations under the License.</span>
|
||||
<span class="c1">#</span>
|
||||
|
||||
<span class="kn">import</span> <span class="nn">operator</span>
|
||||
<span class="kn">import</span> <span class="nn">random</span>
|
||||
<span class="kn">from</span> <span class="nn">enum</span> <span class="k">import</span> <span class="n">Enum</span>
|
||||
<span class="kn">from</span> <span class="nn">typing</span> <span class="k">import</span> <span class="n">List</span><span class="p">,</span> <span class="n">Tuple</span><span class="p">,</span> <span class="n">Any</span>
|
||||
|
||||
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
|
||||
|
||||
<span class="kn">from</span> <span class="nn">rl_coach.core_types</span> <span class="k">import</span> <span class="n">Transition</span>
|
||||
<span class="kn">from</span> <span class="nn">rl_coach.memories.memory</span> <span class="k">import</span> <span class="n">MemoryGranularity</span>
|
||||
<span class="kn">from</span> <span class="nn">rl_coach.memories.non_episodic.experience_replay</span> <span class="k">import</span> <span class="n">ExperienceReplayParameters</span><span class="p">,</span> <span class="n">ExperienceReplay</span>
|
||||
<span class="kn">from</span> <span class="nn">rl_coach.schedules</span> <span class="k">import</span> <span class="n">Schedule</span><span class="p">,</span> <span class="n">ConstantSchedule</span>
|
||||
|
||||
|
||||
<span class="k">class</span> <span class="nc">PrioritizedExperienceReplayParameters</span><span class="p">(</span><span class="n">ExperienceReplayParameters</span><span class="p">):</span>
|
||||
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">max_size</span> <span class="o">=</span> <span class="p">(</span><span class="n">MemoryGranularity</span><span class="o">.</span><span class="n">Transitions</span><span class="p">,</span> <span class="mi">1000000</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">alpha</span> <span class="o">=</span> <span class="mf">0.6</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">beta</span> <span class="o">=</span> <span class="n">ConstantSchedule</span><span class="p">(</span><span class="mf">0.4</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">epsilon</span> <span class="o">=</span> <span class="mf">1e-6</span>
|
||||
|
||||
<span class="nd">@property</span>
|
||||
<span class="k">def</span> <span class="nf">path</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="k">return</span> <span class="s1">'rl_coach.memories.non_episodic.prioritized_experience_replay:PrioritizedExperienceReplay'</span>
|
||||
|
||||
|
||||
<span class="k">class</span> <span class="nc">SegmentTree</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> A tree which can be used as a min/max heap or a sum tree</span>
|
||||
<span class="sd"> Add or update item value - O(log N)</span>
|
||||
<span class="sd"> Sampling an item - O(log N)</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">class</span> <span class="nc">Operation</span><span class="p">(</span><span class="n">Enum</span><span class="p">):</span>
|
||||
<span class="n">MAX</span> <span class="o">=</span> <span class="p">{</span><span class="s2">"operator"</span><span class="p">:</span> <span class="nb">max</span><span class="p">,</span> <span class="s2">"initial_value"</span><span class="p">:</span> <span class="o">-</span><span class="nb">float</span><span class="p">(</span><span class="s2">"inf"</span><span class="p">)}</span>
|
||||
<span class="n">MIN</span> <span class="o">=</span> <span class="p">{</span><span class="s2">"operator"</span><span class="p">:</span> <span class="nb">min</span><span class="p">,</span> <span class="s2">"initial_value"</span><span class="p">:</span> <span class="nb">float</span><span class="p">(</span><span class="s2">"inf"</span><span class="p">)}</span>
|
||||
<span class="n">SUM</span> <span class="o">=</span> <span class="p">{</span><span class="s2">"operator"</span><span class="p">:</span> <span class="n">operator</span><span class="o">.</span><span class="n">add</span><span class="p">,</span> <span class="s2">"initial_value"</span><span class="p">:</span> <span class="mi">0</span><span class="p">}</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">size</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">operation</span><span class="p">:</span> <span class="n">Operation</span><span class="p">):</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">next_leaf_idx_to_write</span> <span class="o">=</span> <span class="mi">0</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">size</span> <span class="o">=</span> <span class="n">size</span>
|
||||
<span class="k">if</span> <span class="ow">not</span> <span class="p">(</span><span class="n">size</span> <span class="o">></span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">size</span> <span class="o">&</span> <span class="p">(</span><span class="n">size</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">):</span>
|
||||
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"A segment tree size must be a positive power of 2. The given size is </span><span class="si">{}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">size</span><span class="p">))</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">operation</span> <span class="o">=</span> <span class="n">operation</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">tree</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">size</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">operation</span><span class="o">.</span><span class="n">value</span><span class="p">[</span><span class="s1">'initial_value'</span><span class="p">]</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">data</span> <span class="o">=</span> <span class="p">[</span><span class="kc">None</span><span class="p">]</span> <span class="o">*</span> <span class="n">size</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">_propagate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">node_idx</span><span class="p">:</span> <span class="nb">int</span><span class="p">)</span> <span class="o">-></span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Propagate an update of a node's value to its parent node</span>
|
||||
<span class="sd"> :param node_idx: the index of the node that was updated</span>
|
||||
<span class="sd"> :return: None</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">parent</span> <span class="o">=</span> <span class="p">(</span><span class="n">node_idx</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">//</span> <span class="mi">2</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">tree</span><span class="p">[</span><span class="n">parent</span><span class="p">]</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">operation</span><span class="o">.</span><span class="n">value</span><span class="p">[</span><span class="s1">'operator'</span><span class="p">](</span><span class="bp">self</span><span class="o">.</span><span class="n">tree</span><span class="p">[</span><span class="n">parent</span> <span class="o">*</span> <span class="mi">2</span> <span class="o">+</span> <span class="mi">1</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">tree</span><span class="p">[</span><span class="n">parent</span> <span class="o">*</span> <span class="mi">2</span> <span class="o">+</span> <span class="mi">2</span><span class="p">])</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">parent</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_propagate</span><span class="p">(</span><span class="n">parent</span><span class="p">)</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">_retrieve</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">root_node_idx</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">val</span><span class="p">:</span> <span class="nb">float</span><span class="p">)</span><span class="o">-></span> <span class="nb">int</span><span class="p">:</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Retrieve the first node that has a value larger than val and is a child of the node at index idx</span>
|
||||
<span class="sd"> :param root_node_idx: the index of the root node to search from</span>
|
||||
<span class="sd"> :param val: the value to query for</span>
|
||||
<span class="sd"> :return: the index of the resulting node</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">left</span> <span class="o">=</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">root_node_idx</span> <span class="o">+</span> <span class="mi">1</span>
|
||||
<span class="n">right</span> <span class="o">=</span> <span class="n">left</span> <span class="o">+</span> <span class="mi">1</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">left</span> <span class="o">>=</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">tree</span><span class="p">):</span>
|
||||
<span class="k">return</span> <span class="n">root_node_idx</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">val</span> <span class="o"><=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tree</span><span class="p">[</span><span class="n">left</span><span class="p">]:</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_retrieve</span><span class="p">(</span><span class="n">left</span><span class="p">,</span> <span class="n">val</span><span class="p">)</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_retrieve</span><span class="p">(</span><span class="n">right</span><span class="p">,</span> <span class="n">val</span><span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">tree</span><span class="p">[</span><span class="n">left</span><span class="p">])</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">total_value</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-></span> <span class="nb">float</span><span class="p">:</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Return the total value of the tree according to the tree operation. For SUM for example, this will return</span>
|
||||
<span class="sd"> the total sum of the tree. for MIN, this will return the minimal value</span>
|
||||
<span class="sd"> :return: the total value of the tree</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">tree</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">add</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">val</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">Any</span><span class="p">)</span> <span class="o">-></span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Add a new value to the tree with data assigned to it</span>
|
||||
<span class="sd"> :param val: the new value to add to the tree</span>
|
||||
<span class="sd"> :param data: the data that should be assigned to this value</span>
|
||||
<span class="sd"> :return: None</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">next_leaf_idx_to_write</span><span class="p">]</span> <span class="o">=</span> <span class="n">data</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">next_leaf_idx_to_write</span><span class="p">,</span> <span class="n">val</span><span class="p">)</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">next_leaf_idx_to_write</span> <span class="o">+=</span> <span class="mi">1</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">next_leaf_idx_to_write</span> <span class="o">>=</span> <span class="bp">self</span><span class="o">.</span><span class="n">size</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">next_leaf_idx_to_write</span> <span class="o">=</span> <span class="mi">0</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">update</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">leaf_idx</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">new_val</span><span class="p">:</span> <span class="nb">float</span><span class="p">)</span> <span class="o">-></span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Update the value of the node at index idx</span>
|
||||
<span class="sd"> :param leaf_idx: the index of the node to update</span>
|
||||
<span class="sd"> :param new_val: the new value of the node</span>
|
||||
<span class="sd"> :return: None</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">node_idx</span> <span class="o">=</span> <span class="n">leaf_idx</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">size</span> <span class="o">-</span> <span class="mi">1</span>
|
||||
<span class="k">if</span> <span class="ow">not</span> <span class="mi">0</span> <span class="o"><=</span> <span class="n">node_idx</span> <span class="o"><</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">tree</span><span class="p">):</span>
|
||||
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"The given left index (</span><span class="si">{}</span><span class="s2">) can not be found in the tree. The available leaves are: 0-</span><span class="si">{}</span><span class="s2">"</span>
|
||||
<span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">leaf_idx</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">size</span> <span class="o">-</span> <span class="mi">1</span><span class="p">))</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">tree</span><span class="p">[</span><span class="n">node_idx</span><span class="p">]</span> <span class="o">=</span> <span class="n">new_val</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_propagate</span><span class="p">(</span><span class="n">node_idx</span><span class="p">)</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">get_element_by_partial_sum</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">val</span><span class="p">:</span> <span class="nb">float</span><span class="p">)</span> <span class="o">-></span> <span class="n">Tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">float</span><span class="p">,</span> <span class="n">Any</span><span class="p">]:</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Given a value between 0 and the tree sum, return the object which this value is in it's range.</span>
|
||||
<span class="sd"> For example, if we have 3 leaves: 10, 20, 30, and val=35, this will return the 3rd leaf, by accumulating</span>
|
||||
<span class="sd"> leaves by their order until getting to 35. This allows sampling leaves according to their proportional</span>
|
||||
<span class="sd"> probability.</span>
|
||||
<span class="sd"> :param val: a value within the range 0 and the tree sum</span>
|
||||
<span class="sd"> :return: the index of the resulting leaf in the tree, its probability and</span>
|
||||
<span class="sd"> the object itself</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="n">node_idx</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_retrieve</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">val</span><span class="p">)</span>
|
||||
<span class="n">leaf_idx</span> <span class="o">=</span> <span class="n">node_idx</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">size</span> <span class="o">+</span> <span class="mi">1</span>
|
||||
<span class="n">data_value</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">tree</span><span class="p">[</span><span class="n">node_idx</span><span class="p">]</span>
|
||||
<span class="n">data</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="n">leaf_idx</span><span class="p">]</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">leaf_idx</span><span class="p">,</span> <span class="n">data_value</span><span class="p">,</span> <span class="n">data</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">__str__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="n">result</span> <span class="o">=</span> <span class="s2">""</span>
|
||||
<span class="n">start</span> <span class="o">=</span> <span class="mi">0</span>
|
||||
<span class="n">size</span> <span class="o">=</span> <span class="mi">1</span>
|
||||
<span class="k">while</span> <span class="n">size</span> <span class="o"><=</span> <span class="bp">self</span><span class="o">.</span><span class="n">size</span><span class="p">:</span>
|
||||
<span class="n">result</span> <span class="o">+=</span> <span class="s2">"</span><span class="si">{}</span><span class="se">\n</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">tree</span><span class="p">[</span><span class="n">start</span><span class="p">:(</span><span class="n">start</span> <span class="o">+</span> <span class="n">size</span><span class="p">)])</span>
|
||||
<span class="n">start</span> <span class="o">+=</span> <span class="n">size</span>
|
||||
<span class="n">size</span> <span class="o">*=</span> <span class="mi">2</span>
|
||||
<span class="k">return</span> <span class="n">result</span>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="PrioritizedExperienceReplay"><a class="viewcode-back" href="../../../../components/memories/index.html#rl_coach.memories.non_episodic.PrioritizedExperienceReplay">[docs]</a><span class="k">class</span> <span class="nc">PrioritizedExperienceReplay</span><span class="p">(</span><span class="n">ExperienceReplay</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> This is the proportional sampling variant of the prioritized experience replay as described</span>
|
||||
<span class="sd"> in https://arxiv.org/pdf/1511.05952.pdf.</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">max_size</span><span class="p">:</span> <span class="n">Tuple</span><span class="p">[</span><span class="n">MemoryGranularity</span><span class="p">,</span> <span class="nb">int</span><span class="p">],</span> <span class="n">alpha</span><span class="p">:</span> <span class="nb">float</span><span class="o">=</span><span class="mf">0.6</span><span class="p">,</span> <span class="n">beta</span><span class="p">:</span> <span class="n">Schedule</span><span class="o">=</span><span class="n">ConstantSchedule</span><span class="p">(</span><span class="mf">0.4</span><span class="p">),</span>
|
||||
<span class="n">epsilon</span><span class="p">:</span> <span class="nb">float</span><span class="o">=</span><span class="mf">1e-6</span><span class="p">,</span> <span class="n">allow_duplicates_in_batch_sampling</span><span class="p">:</span> <span class="nb">bool</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> :param max_size: the maximum number of transitions or episodes to hold in the memory</span>
|
||||
<span class="sd"> :param alpha: the alpha prioritization coefficient</span>
|
||||
<span class="sd"> :param beta: the beta parameter used for importance sampling</span>
|
||||
<span class="sd"> :param epsilon: a small value added to the priority of each transition</span>
|
||||
<span class="sd"> :param allow_duplicates_in_batch_sampling: allow having the same transition multiple times in a batch</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">if</span> <span class="n">max_size</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">!=</span> <span class="n">MemoryGranularity</span><span class="o">.</span><span class="n">Transitions</span><span class="p">:</span>
|
||||
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"Prioritized Experience Replay currently only support setting the memory size in "</span>
|
||||
<span class="s2">"transitions granularity."</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">power_of_2_size</span> <span class="o">=</span> <span class="mi">1</span>
|
||||
<span class="k">while</span> <span class="bp">self</span><span class="o">.</span><span class="n">power_of_2_size</span> <span class="o"><</span> <span class="n">max_size</span><span class="p">[</span><span class="mi">1</span><span class="p">]:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">power_of_2_size</span> <span class="o">*=</span> <span class="mi">2</span>
|
||||
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">((</span><span class="n">MemoryGranularity</span><span class="o">.</span><span class="n">Transitions</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">power_of_2_size</span><span class="p">),</span> <span class="n">allow_duplicates_in_batch_sampling</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">sum_tree</span> <span class="o">=</span> <span class="n">SegmentTree</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">power_of_2_size</span><span class="p">,</span> <span class="n">SegmentTree</span><span class="o">.</span><span class="n">Operation</span><span class="o">.</span><span class="n">SUM</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">min_tree</span> <span class="o">=</span> <span class="n">SegmentTree</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">power_of_2_size</span><span class="p">,</span> <span class="n">SegmentTree</span><span class="o">.</span><span class="n">Operation</span><span class="o">.</span><span class="n">MIN</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">max_tree</span> <span class="o">=</span> <span class="n">SegmentTree</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">power_of_2_size</span><span class="p">,</span> <span class="n">SegmentTree</span><span class="o">.</span><span class="n">Operation</span><span class="o">.</span><span class="n">MAX</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">alpha</span> <span class="o">=</span> <span class="n">alpha</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">beta</span> <span class="o">=</span> <span class="n">beta</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">epsilon</span> <span class="o">=</span> <span class="n">epsilon</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">maximal_priority</span> <span class="o">=</span> <span class="mf">1.0</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">_update_priority</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">leaf_idx</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">error</span><span class="p">:</span> <span class="nb">float</span><span class="p">)</span> <span class="o">-></span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Update the priority of a given transition, using its index in the tree and its error</span>
|
||||
<span class="sd"> :param leaf_idx: the index of the transition leaf in the tree</span>
|
||||
<span class="sd"> :param error: the new error value</span>
|
||||
<span class="sd"> :return: None</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">if</span> <span class="n">error</span> <span class="o"><</span> <span class="mi">0</span><span class="p">:</span>
|
||||
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"The priorities must be non-negative values"</span><span class="p">)</span>
|
||||
<span class="n">priority</span> <span class="o">=</span> <span class="p">(</span><span class="n">error</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">epsilon</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">sum_tree</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">leaf_idx</span><span class="p">,</span> <span class="n">priority</span> <span class="o">**</span> <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">min_tree</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">leaf_idx</span><span class="p">,</span> <span class="n">priority</span> <span class="o">**</span> <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">max_tree</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">leaf_idx</span><span class="p">,</span> <span class="n">priority</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">maximal_priority</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">max_tree</span><span class="o">.</span><span class="n">total_value</span><span class="p">()</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">update_priorities</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">indices</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="nb">int</span><span class="p">],</span> <span class="n">error_values</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="nb">float</span><span class="p">])</span> <span class="o">-></span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Update the priorities of a batch of transitions using their indices and their new TD error terms</span>
|
||||
<span class="sd"> :param indices: the indices of the transitions to update</span>
|
||||
<span class="sd"> :param error_values: the new error values</span>
|
||||
<span class="sd"> :return: None</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">reader_writer_lock</span><span class="o">.</span><span class="n">lock_writing_and_reading</span><span class="p">()</span>
|
||||
|
||||
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">indices</span><span class="p">)</span> <span class="o">!=</span> <span class="nb">len</span><span class="p">(</span><span class="n">error_values</span><span class="p">):</span>
|
||||
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"The number of indexes requested for update don't match the number of error values given"</span><span class="p">)</span>
|
||||
<span class="k">for</span> <span class="n">transition_idx</span><span class="p">,</span> <span class="n">error</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">indices</span><span class="p">,</span> <span class="n">error_values</span><span class="p">):</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_update_priority</span><span class="p">(</span><span class="n">transition_idx</span><span class="p">,</span> <span class="n">error</span><span class="p">)</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">reader_writer_lock</span><span class="o">.</span><span class="n">release_writing_and_reading</span><span class="p">()</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">sample</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">size</span><span class="p">:</span> <span class="nb">int</span><span class="p">)</span> <span class="o">-></span> <span class="n">List</span><span class="p">[</span><span class="n">Transition</span><span class="p">]:</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Sample a batch of transitions form the replay buffer. If the requested size is larger than the number</span>
|
||||
<span class="sd"> of samples available in the replay buffer then the batch will return empty.</span>
|
||||
<span class="sd"> :param size: the size of the batch to sample</span>
|
||||
<span class="sd"> :return: a batch (list) of selected transitions from the replay buffer</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">reader_writer_lock</span><span class="o">.</span><span class="n">lock_writing</span><span class="p">()</span>
|
||||
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_transitions</span><span class="p">()</span> <span class="o">>=</span> <span class="n">size</span><span class="p">:</span>
|
||||
<span class="c1"># split the tree leaves to equal segments and sample one transition from each segment</span>
|
||||
<span class="n">batch</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="n">segment_size</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sum_tree</span><span class="o">.</span><span class="n">total_value</span><span class="p">()</span> <span class="o">/</span> <span class="n">size</span>
|
||||
|
||||
<span class="c1"># get the maximum weight in the memory</span>
|
||||
<span class="n">min_probability</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">min_tree</span><span class="o">.</span><span class="n">total_value</span><span class="p">()</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">sum_tree</span><span class="o">.</span><span class="n">total_value</span><span class="p">()</span> <span class="c1"># min P(j) = min p^a / sum(p^a)</span>
|
||||
<span class="n">max_weight</span> <span class="o">=</span> <span class="p">(</span><span class="n">min_probability</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_transitions</span><span class="p">())</span> <span class="o">**</span> <span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">beta</span><span class="o">.</span><span class="n">current_value</span> <span class="c1"># max wi</span>
|
||||
|
||||
<span class="c1"># sample a batch</span>
|
||||
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">size</span><span class="p">):</span>
|
||||
<span class="n">segment_start</span> <span class="o">=</span> <span class="n">segment_size</span> <span class="o">*</span> <span class="n">i</span>
|
||||
<span class="n">segment_end</span> <span class="o">=</span> <span class="n">segment_size</span> <span class="o">*</span> <span class="p">(</span><span class="n">i</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
|
||||
|
||||
<span class="c1"># sample leaf and calculate its weight</span>
|
||||
<span class="n">val</span> <span class="o">=</span> <span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="n">segment_start</span><span class="p">,</span> <span class="n">segment_end</span><span class="p">)</span>
|
||||
<span class="n">leaf_idx</span><span class="p">,</span> <span class="n">priority</span><span class="p">,</span> <span class="n">transition</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sum_tree</span><span class="o">.</span><span class="n">get_element_by_partial_sum</span><span class="p">(</span><span class="n">val</span><span class="p">)</span>
|
||||
<span class="n">priority</span> <span class="o">/=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sum_tree</span><span class="o">.</span><span class="n">total_value</span><span class="p">()</span> <span class="c1"># P(j) = p^a / sum(p^a)</span>
|
||||
<span class="n">weight</span> <span class="o">=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">num_transitions</span><span class="p">()</span> <span class="o">*</span> <span class="n">priority</span><span class="p">)</span> <span class="o">**</span> <span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">beta</span><span class="o">.</span><span class="n">current_value</span> <span class="c1"># (N * P(j)) ^ -beta</span>
|
||||
<span class="n">normalized_weight</span> <span class="o">=</span> <span class="n">weight</span> <span class="o">/</span> <span class="n">max_weight</span> <span class="c1"># wj = ((N * P(j)) ^ -beta) / max wi</span>
|
||||
|
||||
<span class="n">transition</span><span class="o">.</span><span class="n">info</span><span class="p">[</span><span class="s1">'idx'</span><span class="p">]</span> <span class="o">=</span> <span class="n">leaf_idx</span>
|
||||
<span class="n">transition</span><span class="o">.</span><span class="n">info</span><span class="p">[</span><span class="s1">'weight'</span><span class="p">]</span> <span class="o">=</span> <span class="n">normalized_weight</span>
|
||||
|
||||
<span class="n">batch</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">transition</span><span class="p">)</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">beta</span><span class="o">.</span><span class="n">step</span><span class="p">()</span>
|
||||
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"The replay buffer cannot be sampled since there are not enough transitions yet. "</span>
|
||||
<span class="s2">"There are currently </span><span class="si">{}</span><span class="s2"> transitions"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">num_transitions</span><span class="p">()))</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">reader_writer_lock</span><span class="o">.</span><span class="n">release_writing</span><span class="p">()</span>
|
||||
<span class="k">return</span> <span class="n">batch</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">store</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">transition</span><span class="p">:</span> <span class="n">Transition</span><span class="p">,</span> <span class="n">lock</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="o">-></span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Store a new transition in the memory.</span>
|
||||
<span class="sd"> :param transition: a transition to store</span>
|
||||
<span class="sd"> :return: None</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="c1"># Calling super.store() so that in case a memory backend is used, the memory backend can store this transition.</span>
|
||||
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">store</span><span class="p">(</span><span class="n">transition</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">lock</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">reader_writer_lock</span><span class="o">.</span><span class="n">lock_writing_and_reading</span><span class="p">()</span>
|
||||
|
||||
<span class="n">transition_priority</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">maximal_priority</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">sum_tree</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">transition_priority</span> <span class="o">**</span> <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span><span class="p">,</span> <span class="n">transition</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">min_tree</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">transition_priority</span> <span class="o">**</span> <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span><span class="p">,</span> <span class="n">transition</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">max_tree</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">transition_priority</span><span class="p">,</span> <span class="n">transition</span><span class="p">)</span>
|
||||
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">store</span><span class="p">(</span><span class="n">transition</span><span class="p">,</span> <span class="kc">False</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">lock</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">reader_writer_lock</span><span class="o">.</span><span class="n">release_writing_and_reading</span><span class="p">()</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">clean</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">lock</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="o">-></span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Clean the memory by removing all the episodes</span>
|
||||
<span class="sd"> :return: None</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">if</span> <span class="n">lock</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">reader_writer_lock</span><span class="o">.</span><span class="n">lock_writing_and_reading</span><span class="p">()</span>
|
||||
|
||||
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">clean</span><span class="p">(</span><span class="n">lock</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">sum_tree</span> <span class="o">=</span> <span class="n">SegmentTree</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">power_of_2_size</span><span class="p">,</span> <span class="n">SegmentTree</span><span class="o">.</span><span class="n">Operation</span><span class="o">.</span><span class="n">SUM</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">min_tree</span> <span class="o">=</span> <span class="n">SegmentTree</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">power_of_2_size</span><span class="p">,</span> <span class="n">SegmentTree</span><span class="o">.</span><span class="n">Operation</span><span class="o">.</span><span class="n">MIN</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">max_tree</span> <span class="o">=</span> <span class="n">SegmentTree</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">power_of_2_size</span><span class="p">,</span> <span class="n">SegmentTree</span><span class="o">.</span><span class="n">Operation</span><span class="o">.</span><span class="n">MAX</span><span class="p">)</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">lock</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">reader_writer_lock</span><span class="o">.</span><span class="n">release_writing_and_reading</span><span class="p">()</span></div>
|
||||
</pre></div>
|
||||
|
||||
</div>
|
||||
|
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<h1>Source code for rl_coach.memories.non_episodic.transition_collection</h1><div class="highlight"><pre>
|
||||
<span></span><span class="kn">from</span> <span class="nn">rl_coach.core_types</span> <span class="k">import</span> <span class="n">Transition</span>
|
||||
|
||||
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||||
<div class="viewcode-block" id="TransitionCollection"><a class="viewcode-back" href="../../../../components/memories/index.html#rl_coach.memories.non_episodic.TransitionCollection">[docs]</a><span class="k">class</span> <span class="nc">TransitionCollection</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Simple python implementation of transitions collection non-episodic memories</span>
|
||||
<span class="sd"> are constructed on top of.</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="nb">super</span><span class="p">(</span><span class="n">TransitionCollection</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">append</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">transition</span><span class="p">):</span>
|
||||
<span class="k">pass</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">extend</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">transitions</span><span class="p">):</span>
|
||||
<span class="k">for</span> <span class="n">transition</span> <span class="ow">in</span> <span class="n">transitions</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">transition</span><span class="p">)</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">__len__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="k">pass</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">__del__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="nb">range</span><span class="p">:</span> <span class="nb">slice</span><span class="p">):</span>
|
||||
<span class="c1"># NOTE: the only slice used is the form: slice(None, n)</span>
|
||||
<span class="c1"># NOTE: if it is easier, what we really want here is the ability to</span>
|
||||
<span class="c1"># constrain the size of the collection. as new transitions are added,</span>
|
||||
<span class="c1"># old transitions can be removed to maintain a maximum collection size.</span>
|
||||
<span class="k">pass</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">__getitem__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">key</span><span class="p">:</span> <span class="nb">int</span><span class="p">):</span>
|
||||
<span class="c1"># NOTE: we can switch to a method which fetches multiple items at a time</span>
|
||||
<span class="c1"># if that would significantly improve performance</span>
|
||||
<span class="k">pass</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">__iter__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="c1"># this is not high priority</span>
|
||||
<span class="k">pass</span></div>
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