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<h1>Source code for rl_coach.agents.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.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">QHeadParameters</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">NetworkParameters</span><span class="p">,</span> <span class="n">AgentParameters</span><span class="p">,</span> \
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<span class="n">MiddlewareScheme</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">EnvironmentSteps</span>
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<span class="kn">from</span> <span class="nn">rl_coach.exploration_policies.e_greedy</span> <span class="k">import</span> <span class="n">EGreedyParameters</span>
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<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>
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<span class="kn">from</span> <span class="nn">rl_coach.schedules</span> <span class="k">import</span> <span class="n">LinearSchedule</span>
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<div class="viewcode-block" id="DQNAlgorithmParameters"><a class="viewcode-back" href="../../../components/agents/value_optimization/dqn.html#rl_coach.agents.dqn_agent.DQNAlgorithmParameters">[docs]</a><span class="k">class</span> <span class="nc">DQNAlgorithmParameters</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_steps_between_copying_online_weights_to_target</span> <span class="o">=</span> <span class="n">EnvironmentSteps</span><span class="p">(</span><span class="mi">10000</span><span class="p">)</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">num_consecutive_playing_steps</span> <span class="o">=</span> <span class="n">EnvironmentSteps</span><span class="p">(</span><span class="mi">4</span><span class="p">)</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">discount</span> <span class="o">=</span> <span class="mf">0.99</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">supports_parameter_noise</span> <span class="o">=</span> <span class="kc">True</span></div>
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<span class="k">class</span> <span class="nc">DQNNetworkParameters</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><span class="n">scheme</span><span class="o">=</span><span class="n">MiddlewareScheme</span><span class="o">.</span><span class="n">Medium</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">QHeadParameters</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">batch_size</span> <span class="o">=</span> <span class="mi">32</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">replace_mse_with_huber_loss</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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<span class="bp">self</span><span class="o">.</span><span class="n">should_get_softmax_probabilities</span> <span class="o">=</span> <span class="kc">False</span>
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<span class="k">class</span> <span class="nc">DQNAgentParameters</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">DQNAlgorithmParameters</span><span class="p">(),</span>
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<span class="n">exploration</span><span class="o">=</span><span class="n">EGreedyParameters</span><span class="p">(),</span>
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<span class="n">memory</span><span class="o">=</span><span class="n">ExperienceReplayParameters</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">DQNNetworkParameters</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">epsilon_schedule</span> <span class="o">=</span> <span class="n">LinearSchedule</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mf">0.1</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">exploration</span><span class="o">.</span><span class="n">evaluation_epsilon</span> <span class="o">=</span> <span class="mf">0.05</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.dqn_agent:DQNAgent'</span>
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<span class="c1"># Deep Q Network - https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf</span>
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<div class="viewcode-block" id="DQNAgent"><a class="viewcode-back" href="../../../test.html#rl_coach.agents.dqn_agent.DQNAgent">[docs]</a><span class="k">class</span> <span class="nc">DQNAgent</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="k">def</span> <span class="nf">select_actions</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">next_states</span><span class="p">,</span> <span class="n">q_st_plus_1</span><span class="p">):</span>
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<span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">q_st_plus_1</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
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<div class="viewcode-block" id="DQNAgent.learn_from_batch"><a class="viewcode-back" href="../../../test.html#rl_coach.agents.dqn_agent.DQNAgent.learn_from_batch">[docs]</a> <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"># for the action we actually took, the error is:</span>
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<span class="c1"># TD error = r + discount*max(q_st_plus_1) - q_st</span>
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<span class="c1"># # for all other actions, the error is 0</span>
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<span class="n">q_st_plus_1</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="n">selected_actions</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">select_actions</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">q_st_plus_1</span><span class="p">)</span>
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<span class="c1"># add Q value samples 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">TD_targets</span><span class="p">)</span>
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<span class="c1"># only update the action that we have actually done in this transition</span>
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<span class="n">TD_errors</span> <span class="o">=</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="n">batch</span><span class="o">.</span><span class="n">size</span><span class="p">):</span>
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<span class="n">new_target</span> <span class="o">=</span> <span class="n">batch</span><span class="o">.</span><span class="n">rewards</span><span class="p">()[</span><span class="n">i</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">batch</span><span class="o">.</span><span class="n">game_overs</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">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">q_st_plus_1</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="n">selected_actions</span><span class="p">[</span><span class="n">i</span><span class="p">]]</span>
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<span class="n">TD_errors</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">new_target</span> <span class="o">-</span> <span class="n">TD_targets</span><span class="p">[</span><span class="n">i</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="n">i</span><span class="p">]]))</span>
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<span class="n">TD_targets</span><span class="p">[</span><span class="n">i</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="n">i</span><span class="p">]]</span> <span class="o">=</span> <span class="n">new_target</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="bp">self</span><span class="o">.</span><span class="n">update_transition_priorities_and_get_weights</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="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="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></div></div>
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</pre></div>
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