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* 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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<h1>Source code for rl_coach.agents.rainbow_dqn_agent</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">Union</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.agents.categorical_dqn_agent</span> <span class="k">import</span> <span class="n">CategoricalDQNAlgorithmParameters</span><span class="p">,</span> \
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<span class="n">CategoricalDQNAgent</span><span class="p">,</span> <span class="n">CategoricalDQNAgentParameters</span>
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<span class="kn">from</span> <span class="nn">rl_coach.agents.dqn_agent</span> <span class="k">import</span> <span class="n">DQNNetworkParameters</span>
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<span class="kn">from</span> <span class="nn">rl_coach.architectures.head_parameters</span> <span class="k">import</span> <span class="n">RainbowQHeadParameters</span>
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<span class="kn">from</span> <span class="nn">rl_coach.architectures.middleware_parameters</span> <span class="k">import</span> <span class="n">FCMiddlewareParameters</span>
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<span class="kn">from</span> <span class="nn">rl_coach.base_parameters</span> <span class="k">import</span> <span class="n">MiddlewareScheme</span>
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<span class="kn">from</span> <span class="nn">rl_coach.exploration_policies.parameter_noise</span> <span class="k">import</span> <span class="n">ParameterNoiseParameters</span>
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<span class="kn">from</span> <span class="nn">rl_coach.memories.non_episodic.prioritized_experience_replay</span> <span class="k">import</span> <span class="n">PrioritizedExperienceReplayParameters</span><span class="p">,</span> \
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<span class="n">PrioritizedExperienceReplay</span>
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<span class="k">class</span> <span class="nc">RainbowDQNNetworkParameters</span><span class="p">(</span><span class="n">DQNNetworkParameters</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">heads_parameters</span> <span class="o">=</span> <span class="p">[</span><span class="n">RainbowQHeadParameters</span><span class="p">()]</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">middleware_parameters</span> <span class="o">=</span> <span class="n">FCMiddlewareParameters</span><span class="p">(</span><span class="n">scheme</span><span class="o">=</span><span class="n">MiddlewareScheme</span><span class="o">.</span><span class="n">Empty</span><span class="p">)</span>
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<div class="viewcode-block" id="RainbowDQNAlgorithmParameters"><a class="viewcode-back" href="../../../components/agents/value_optimization/rainbow.html#rl_coach.agents.rainbow_dqn_agent.RainbowDQNAlgorithmParameters">[docs]</a><span class="k">class</span> <span class="nc">RainbowDQNAlgorithmParameters</span><span class="p">(</span><span class="n">CategoricalDQNAlgorithmParameters</span><span class="p">):</span>
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<span class="sd">"""</span>
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<span class="sd"> :param n_step: (int)</span>
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<span class="sd"> The number of steps to bootstrap the network over. The first N-1 steps actual rewards will be accumulated</span>
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<span class="sd"> using an exponentially growing discount factor, and the Nth step will be bootstrapped from the network</span>
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<span class="sd"> prediction.</span>
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<span class="sd"> :param store_transitions_only_when_episodes_are_terminated: (bool)</span>
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<span class="sd"> If set to True, the transitions will be stored in an Episode object until the episode ends, and just then</span>
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<span class="sd"> written to the memory. This is useful since we want to calculate the N-step discounted rewards before saving the</span>
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<span class="sd"> transitions into the memory, and to do so we need the entire episode first.</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>
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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">n_step</span> <span class="o">=</span> <span class="mi">3</span>
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<span class="c1"># needed for n-step updates to work. i.e. waiting for a full episode to be closed before storing each transition</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">store_transitions_only_when_episodes_are_terminated</span> <span class="o">=</span> <span class="kc">True</span></div>
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<span class="k">class</span> <span class="nc">RainbowDQNAgentParameters</span><span class="p">(</span><span class="n">CategoricalDQNAgentParameters</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">algorithm</span> <span class="o">=</span> <span class="n">RainbowDQNAlgorithmParameters</span><span class="p">()</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">exploration</span> <span class="o">=</span> <span class="n">ParameterNoiseParameters</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">memory</span> <span class="o">=</span> <span class="n">PrioritizedExperienceReplayParameters</span><span class="p">()</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">network_wrappers</span> <span class="o">=</span> <span class="p">{</span><span class="s2">"main"</span><span class="p">:</span> <span class="n">RainbowDQNNetworkParameters</span><span class="p">()}</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.agents.rainbow_dqn_agent:RainbowDQNAgent'</span>
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<span class="c1"># Rainbow Deep Q Network - https://arxiv.org/abs/1710.02298</span>
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<span class="c1"># Agent implementation is composed of:</span>
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<span class="c1"># 1. NoisyNets</span>
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<span class="c1"># 2. C51</span>
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<span class="c1"># 3. Prioritized ER</span>
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<span class="c1"># 4. DDQN</span>
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<span class="c1"># 5. Dueling DQN</span>
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<span class="c1"># 6. N-step returns</span>
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<span class="k">class</span> <span class="nc">RainbowDQNAgent</span><span class="p">(</span><span class="n">CategoricalDQNAgent</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> <span class="n">agent_parameters</span><span class="p">,</span> <span class="n">parent</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="s1">'LevelManager'</span><span class="p">,</span> <span class="s1">'CompositeAgent'</span><span class="p">]</span><span class="o">=</span><span class="kc">None</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><span class="n">agent_parameters</span><span class="p">,</span> <span class="n">parent</span><span class="p">)</span>
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<span class="k">def</span> <span class="nf">learn_from_batch</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">batch</span><span class="p">):</span>
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<span class="n">network_keys</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">ap</span><span class="o">.</span><span class="n">network_wrappers</span><span class="p">[</span><span class="s1">'main'</span><span class="p">]</span><span class="o">.</span><span class="n">input_embedders_parameters</span><span class="o">.</span><span class="n">keys</span><span class="p">()</span>
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<span class="n">ddqn_selected_actions</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">distribution_prediction_to_q_values</span><span class="p">(</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">networks</span><span class="p">[</span><span class="s1">'main'</span><span class="p">]</span><span class="o">.</span><span class="n">online_network</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">batch</span><span class="o">.</span><span class="n">next_states</span><span class="p">(</span><span class="n">network_keys</span><span class="p">))),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
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<span class="c1"># for the action we actually took, the error is calculated by the atoms distribution</span>
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<span class="c1"># for all other actions, the error is 0</span>
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<span class="n">distributional_q_st_plus_n</span><span class="p">,</span> <span class="n">TD_targets</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">networks</span><span class="p">[</span><span class="s1">'main'</span><span class="p">]</span><span class="o">.</span><span class="n">parallel_prediction</span><span class="p">([</span>
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<span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">networks</span><span class="p">[</span><span class="s1">'main'</span><span class="p">]</span><span class="o">.</span><span class="n">target_network</span><span class="p">,</span> <span class="n">batch</span><span class="o">.</span><span class="n">next_states</span><span class="p">(</span><span class="n">network_keys</span><span class="p">)),</span>
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<span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">networks</span><span class="p">[</span><span class="s1">'main'</span><span class="p">]</span><span class="o">.</span><span class="n">online_network</span><span class="p">,</span> <span class="n">batch</span><span class="o">.</span><span class="n">states</span><span class="p">(</span><span class="n">network_keys</span><span class="p">))</span>
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<span class="p">])</span>
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<span class="c1"># only update the action that we have actually done in this transition (using the Double-DQN selected actions)</span>
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<span class="n">target_actions</span> <span class="o">=</span> <span class="n">ddqn_selected_actions</span>
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<span class="n">m</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="bp">self</span><span class="o">.</span><span class="n">ap</span><span class="o">.</span><span class="n">network_wrappers</span><span class="p">[</span><span class="s1">'main'</span><span class="p">]</span><span class="o">.</span><span class="n">batch_size</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">z_values</span><span class="o">.</span><span class="n">size</span><span class="p">))</span>
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<span class="n">batches</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">ap</span><span class="o">.</span><span class="n">network_wrappers</span><span class="p">[</span><span class="s1">'main'</span><span class="p">]</span><span class="o">.</span><span class="n">batch_size</span><span class="p">)</span>
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<span class="k">for</span> <span class="n">j</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">z_values</span><span class="o">.</span><span class="n">size</span><span class="p">):</span>
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<span class="c1"># we use batch.info('should_bootstrap_next_state') instead of (1 - batch.game_overs()) since with n-step,</span>
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<span class="c1"># we will not bootstrap for the last n-step transitions in the episode</span>
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<span class="n">tzj</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">fmax</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">fmin</span><span class="p">(</span><span class="n">batch</span><span class="o">.</span><span class="n">n_step_discounted_rewards</span><span class="p">()</span> <span class="o">+</span> <span class="n">batch</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s1">'should_bootstrap_next_state'</span><span class="p">)</span> <span class="o">*</span>
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<span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">ap</span><span class="o">.</span><span class="n">algorithm</span><span class="o">.</span><span class="n">discount</span> <span class="o">**</span> <span class="bp">self</span><span class="o">.</span><span class="n">ap</span><span class="o">.</span><span class="n">algorithm</span><span class="o">.</span><span class="n">n_step</span><span class="p">)</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">z_values</span><span class="p">[</span><span class="n">j</span><span class="p">],</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">z_values</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">z_values</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
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<span class="n">bj</span> <span class="o">=</span> <span class="p">(</span><span class="n">tzj</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">z_values</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span><span class="o">/</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">z_values</span><span class="p">[</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">z_values</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
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<span class="n">u</span> <span class="o">=</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="n">bj</span><span class="p">))</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span>
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<span class="n">l</span> <span class="o">=</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">floor</span><span class="p">(</span><span class="n">bj</span><span class="p">))</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">int</span><span class="p">)</span>
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<span class="n">m</span><span class="p">[</span><span class="n">batches</span><span class="p">,</span> <span class="n">l</span><span class="p">]</span> <span class="o">+=</span> <span class="p">(</span><span class="n">distributional_q_st_plus_n</span><span class="p">[</span><span class="n">batches</span><span class="p">,</span> <span class="n">target_actions</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span> <span class="o">*</span> <span class="p">(</span><span class="n">u</span> <span class="o">-</span> <span class="n">bj</span><span class="p">))</span>
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<span class="n">m</span><span class="p">[</span><span class="n">batches</span><span class="p">,</span> <span class="n">u</span><span class="p">]</span> <span class="o">+=</span> <span class="p">(</span><span class="n">distributional_q_st_plus_n</span><span class="p">[</span><span class="n">batches</span><span class="p">,</span> <span class="n">target_actions</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span> <span class="o">*</span> <span class="p">(</span><span class="n">bj</span> <span class="o">-</span> <span class="n">l</span><span class="p">))</span>
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<span class="c1"># total_loss = cross entropy between actual result above and predicted result for the given action</span>
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<span class="n">TD_targets</span><span class="p">[</span><span class="n">batches</span><span class="p">,</span> <span class="n">batch</span><span class="o">.</span><span class="n">actions</span><span class="p">()]</span> <span class="o">=</span> <span class="n">m</span>
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<span class="c1"># update errors in prioritized replay buffer</span>
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<span class="n">importance_weights</span> <span class="o">=</span> <span class="n">batch</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="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">memory</span><span class="p">,</span> <span class="n">PrioritizedExperienceReplay</span><span class="p">)</span> <span class="k">else</span> <span class="kc">None</span>
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<span class="n">result</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">networks</span><span class="p">[</span><span class="s1">'main'</span><span class="p">]</span><span class="o">.</span><span class="n">train_and_sync_networks</span><span class="p">(</span><span class="n">batch</span><span class="o">.</span><span class="n">states</span><span class="p">(</span><span class="n">network_keys</span><span class="p">),</span> <span class="n">TD_targets</span><span class="p">,</span>
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<span class="n">importance_weights</span><span class="o">=</span><span class="n">importance_weights</span><span class="p">)</span>
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<span class="n">total_loss</span><span class="p">,</span> <span class="n">losses</span><span class="p">,</span> <span class="n">unclipped_grads</span> <span class="o">=</span> <span class="n">result</span><span class="p">[:</span><span class="mi">3</span><span class="p">]</span>
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<span class="c1"># TODO: fix this spaghetti code</span>
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<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">memory</span><span class="p">,</span> <span class="n">PrioritizedExperienceReplay</span><span class="p">):</span>
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<span class="n">errors</span> <span class="o">=</span> <span class="n">losses</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">batch</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="n">actions</span><span class="p">()]</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">call_memory</span><span class="p">(</span><span class="s1">'update_priorities'</span><span class="p">,</span> <span class="p">(</span><span class="n">batch</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="n">errors</span><span class="p">))</span>
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<span class="k">return</span> <span class="n">total_loss</span><span class="p">,</span> <span class="n">losses</span><span class="p">,</span> <span class="n">unclipped_grads</span>
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</pre></div>
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