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<h1>Source code for rl_coach.agents.naf_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.value_optimization_agent</span> <span class="k">import</span> <span class="n">ValueOptimizationAgent</span>
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<span class="kn">from</span> <span class="nn">rl_coach.architectures.embedder_parameters</span> <span class="k">import</span> <span class="n">InputEmbedderParameters</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">NAFHeadParameters</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">AlgorithmParameters</span><span class="p">,</span> <span class="n">AgentParameters</span><span class="p">,</span> \
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<span class="n">NetworkParameters</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">EnvironmentSteps</span>
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<span class="kn">from</span> <span class="nn">rl_coach.exploration_policies.ou_process</span> <span class="k">import</span> <span class="n">OUProcessParameters</span>
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<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>
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<span class="kn">from</span> <span class="nn">rl_coach.spaces</span> <span class="k">import</span> <span class="n">BoxActionSpace</span>
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<span class="k">class</span> <span class="nc">NAFNetworkParameters</span><span class="p">(</span><span class="n">NetworkParameters</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">input_embedders_parameters</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'observation'</span><span class="p">:</span> <span class="n">InputEmbedderParameters</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>
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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">NAFHeadParameters</span><span class="p">()]</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">optimizer_type</span> <span class="o">=</span> <span class="s1">'Adam'</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">learning_rate</span> <span class="o">=</span> <span class="mf">0.001</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">async_training</span> <span class="o">=</span> <span class="kc">True</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">create_target_network</span> <span class="o">=</span> <span class="kc">True</span>
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<div class="viewcode-block" id="NAFAlgorithmParameters"><a class="viewcode-back" href="../../../components/agents/value_optimization/naf.html#rl_coach.agents.naf_agent.NAFAlgorithmParameters">[docs]</a><span class="k">class</span> <span class="nc">NAFAlgorithmParameters</span><span class="p">(</span><span class="n">AlgorithmParameters</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">num_consecutive_training_steps</span> <span class="o">=</span> <span class="mi">5</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">num_steps_between_copying_online_weights_to_target</span> <span class="o">=</span> <span class="n">EnvironmentSteps</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">rate_for_copying_weights_to_target</span> <span class="o">=</span> <span class="mf">0.001</span></div>
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<span class="k">class</span> <span class="nc">NAFAgentParameters</span><span class="p">(</span><span class="n">AgentParameters</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><span class="n">algorithm</span><span class="o">=</span><span class="n">NAFAlgorithmParameters</span><span class="p">(),</span>
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<span class="n">exploration</span><span class="o">=</span><span class="n">OUProcessParameters</span><span class="p">(),</span>
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<span class="n">memory</span><span class="o">=</span><span class="n">EpisodicExperienceReplayParameters</span><span class="p">(),</span>
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<span class="n">networks</span><span class="o">=</span><span class="p">{</span><span class="s2">"main"</span><span class="p">:</span> <span class="n">NAFNetworkParameters</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.naf_agent:NAFAgent'</span>
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<span class="c1"># Normalized Advantage Functions - https://arxiv.org/pdf/1603.00748.pdf</span>
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<span class="k">class</span> <span class="nc">NAFAgent</span><span class="p">(</span><span class="n">ValueOptimizationAgent</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">l_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">"L"</span><span class="p">)</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">a_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">"Advantage"</span><span class="p">)</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">mu_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">"Action"</span><span class="p">)</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">v_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">"V"</span><span class="p">)</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">TD_targets</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">"TD targets"</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="c1"># TD error = r + discount*v_st_plus_1 - q_st</span>
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<span class="n">v_st_plus_1</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">target_network</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span>
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<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="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="o">.</span><span class="n">output_heads</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">V</span><span class="p">,</span>
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<span class="n">squeeze_output</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
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<span class="p">)</span>
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<span class="n">TD_targets</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">batch</span><span class="o">.</span><span class="n">rewards</span><span class="p">(),</span> <span class="o">-</span><span class="mi">1</span><span class="p">)</span> <span class="o">+</span> \
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<span class="p">(</span><span class="mf">1.0</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">batch</span><span class="o">.</span><span class="n">game_overs</span><span class="p">(),</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">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="n">v_st_plus_1</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">TD_targets</span><span class="o">.</span><span class="n">add_sample</span><span class="p">(</span><span class="n">TD_targets</span><span class="p">)</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="o">**</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="s1">'output_0_0'</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="nb">len</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">shape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span><span class="p">)</span>
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<span class="p">},</span> <span class="n">TD_targets</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="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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<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="k">if</span> <span class="nb">type</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="o">!=</span> <span class="n">BoxActionSpace</span><span class="p">:</span>
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<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'NAF works only for continuous control problems'</span><span class="p">)</span>
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<span class="c1"># convert to batch so we can run it through the network</span>
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<span class="n">tf_input_state</span> <span class="o">=</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">curr_state</span><span class="p">,</span> <span class="s1">'main'</span><span class="p">)</span>
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<span class="n">naf_head</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">online_network</span><span class="o">.</span><span class="n">output_heads</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
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<span class="n">action_values</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">online_network</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">tf_input_state</span><span class="p">,</span> <span class="n">outputs</span><span class="o">=</span><span class="n">naf_head</span><span class="o">.</span><span class="n">mu</span><span class="p">,</span>
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<span class="n">squeeze_output</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
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<span class="c1"># get the actual action to use</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">action_values</span><span class="p">)</span>
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<span class="c1"># get the internal values for logging</span>
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<span class="n">outputs</span> <span class="o">=</span> <span class="p">[</span><span class="n">naf_head</span><span class="o">.</span><span class="n">mu</span><span class="p">,</span> <span class="n">naf_head</span><span class="o">.</span><span class="n">Q</span><span class="p">,</span> <span class="n">naf_head</span><span class="o">.</span><span class="n">L</span><span class="p">,</span> <span class="n">naf_head</span><span class="o">.</span><span class="n">A</span><span class="p">,</span> <span class="n">naf_head</span><span class="o">.</span><span class="n">V</span><span class="p">]</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">online_network</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span>
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<span class="p">{</span><span class="o">**</span><span class="n">tf_input_state</span><span class="p">,</span> <span class="s1">'output_0_0'</span><span class="p">:</span> <span class="n">action_values</span><span class="p">},</span>
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<span class="n">outputs</span><span class="o">=</span><span class="n">outputs</span>
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<span class="p">)</span>
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<span class="n">mu</span><span class="p">,</span> <span class="n">Q</span><span class="p">,</span> <span class="n">L</span><span class="p">,</span> <span class="n">A</span><span class="p">,</span> <span class="n">V</span> <span class="o">=</span> <span class="n">result</span>
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<span class="c1"># store the q values statistics for logging</span>
|
|
<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">Q</span><span class="p">)</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">l_values</span><span class="o">.</span><span class="n">add_sample</span><span class="p">(</span><span class="n">L</span><span class="p">)</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">a_values</span><span class="o">.</span><span class="n">add_sample</span><span class="p">(</span><span class="n">A</span><span class="p">)</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">mu_values</span><span class="o">.</span><span class="n">add_sample</span><span class="p">(</span><span class="n">mu</span><span class="p">)</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">v_values</span><span class="o">.</span><span class="n">add_sample</span><span class="p">(</span><span class="n">V</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> <span class="n">action_value</span><span class="o">=</span><span class="n">Q</span><span class="p">)</span>
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<span class="k">return</span> <span class="n">action_info</span>
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</pre></div>
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