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<h1>Source code for rl_coach.agents.value_optimization_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.agent</span> <span class="k">import</span> <span class="n">Agent</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">ActionInfo</span><span class="p">,</span> <span class="n">StateType</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">PrioritizedExperienceReplay</span>
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<span class="kn">from</span> <span class="nn">rl_coach.spaces</span> <span class="k">import</span> <span class="n">DiscreteActionSpace</span>
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<span class="c1">## This is an abstract agent - there is no learn_from_batch method ##</span>
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<span class="k">class</span> <span class="nc">ValueOptimizationAgent</span><span class="p">(</span><span class="n">Agent</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="bp">self</span><span class="o">.</span><span class="n">q_values</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">register_signal</span><span class="p">(</span><span class="s2">"Q"</span><span class="p">)</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">q_value_for_action</span> <span class="o">=</span> <span class="p">{}</span>
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<span class="k">def</span> <span class="nf">init_environment_dependent_modules</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="n">init_environment_dependent_modules</span><span class="p">()</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">spaces</span><span class="o">.</span><span class="n">action</span><span class="p">,</span> <span class="n">DiscreteActionSpace</span><span class="p">):</span>
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<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="bp">self</span><span class="o">.</span><span class="n">spaces</span><span class="o">.</span><span class="n">action</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">q_value_for_action</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">register_signal</span><span class="p">(</span><span class="s2">"Q for action </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">i</span><span class="p">),</span>
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<span class="n">dump_one_value_per_episode</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
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<span class="n">dump_one_value_per_step</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
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<span class="c1"># Algorithms for which q_values are calculated from predictions will override this function</span>
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<span class="k">def</span> <span class="nf">get_all_q_values_for_states</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">states</span><span class="p">:</span> <span class="n">StateType</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">exploration_policy</span><span class="o">.</span><span class="n">requires_action_values</span><span class="p">():</span>
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<span class="n">actions_q_values</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_prediction</span><span class="p">(</span><span class="n">states</span><span class="p">)</span>
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<span class="k">else</span><span class="p">:</span>
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<span class="n">actions_q_values</span> <span class="o">=</span> <span class="kc">None</span>
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<span class="k">return</span> <span class="n">actions_q_values</span>
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<span class="k">def</span> <span class="nf">get_prediction</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">states</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">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="bp">self</span><span class="o">.</span><span class="n">prepare_batch_for_inference</span><span class="p">(</span><span class="n">states</span><span class="p">,</span> <span class="s1">'main'</span><span class="p">))</span>
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<span class="k">def</span> <span class="nf">update_transition_priorities_and_get_weights</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">TD_errors</span><span class="p">,</span> <span class="n">batch</span><span class="p">):</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="kc">None</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="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">TD_errors</span><span class="p">))</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>
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<span class="k">return</span> <span class="n">importance_weights</span>
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<span class="k">def</span> <span class="nf">_validate_action</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">policy</span><span class="p">,</span> <span class="n">action</span><span class="p">):</span>
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<span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">action</span><span class="p">)</span><span class="o">.</span><span class="n">shape</span> <span class="o">!=</span> <span class="p">():</span>
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<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">((</span>
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<span class="s1">'The exploration_policy </span><span class="si">{}</span><span class="s1"> returned a vector of actions '</span>
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<span class="s1">'instead of a single action. ValueOptimizationAgents '</span>
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<span class="s1">'require exploration policies which return a single action.'</span>
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<span class="p">)</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">policy</span><span class="o">.</span><span class="vm">__class__</span><span class="o">.</span><span class="vm">__name__</span><span class="p">))</span>
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<span class="k">def</span> <span class="nf">choose_action</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">curr_state</span><span class="p">):</span>
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<span class="n">actions_q_values</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_all_q_values_for_states</span><span class="p">(</span><span class="n">curr_state</span><span class="p">)</span>
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<span class="c1"># choose action according to the exploration policy and the current phase (evaluating or training the agent)</span>
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<span class="n">action</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">exploration_policy</span><span class="o">.</span><span class="n">get_action</span><span class="p">(</span><span class="n">actions_q_values</span><span class="p">)</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">_validate_action</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">exploration_policy</span><span class="p">,</span> <span class="n">action</span><span class="p">)</span>
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<span class="k">if</span> <span class="n">actions_q_values</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
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<span class="c1"># this is for bootstrapped dqn</span>
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<span class="k">if</span> <span class="nb">type</span><span class="p">(</span><span class="n">actions_q_values</span><span class="p">)</span> <span class="o">==</span> <span class="nb">list</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="n">actions_q_values</span><span class="p">)</span> <span class="o">></span> <span class="mi">0</span><span class="p">:</span>
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<span class="n">actions_q_values</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">exploration_policy</span><span class="o">.</span><span class="n">last_action_values</span>
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<span class="n">actions_q_values</span> <span class="o">=</span> <span class="n">actions_q_values</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
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<span class="c1"># store the q values statistics for logging</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">q_values</span><span class="o">.</span><span class="n">add_sample</span><span class="p">(</span><span class="n">actions_q_values</span><span class="p">)</span>
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<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">q_value</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">actions_q_values</span><span class="p">):</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">q_value_for_action</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">add_sample</span><span class="p">(</span><span class="n">q_value</span><span class="p">)</span>
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<span class="n">action_info</span> <span class="o">=</span> <span class="n">ActionInfo</span><span class="p">(</span><span class="n">action</span><span class="o">=</span><span class="n">action</span><span class="p">,</span>
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<span class="n">action_value</span><span class="o">=</span><span class="n">actions_q_values</span><span class="p">[</span><span class="n">action</span><span class="p">],</span>
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<span class="n">max_action_value</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">actions_q_values</span><span class="p">))</span>
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<span class="k">else</span><span class="p">:</span>
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<span class="n">action_info</span> <span class="o">=</span> <span class="n">ActionInfo</span><span class="p">(</span><span class="n">action</span><span class="o">=</span><span class="n">action</span><span class="p">)</span>
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<span class="k">return</span> <span class="n">action_info</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="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="s2">"ValueOptimizationAgent is an abstract agent. Not to be used directly."</span><span class="p">)</span>
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