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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.mmc_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.dqn_agent</span> <span class="k">import</span> <span class="n">DQNAgentParameters</span><span class="p">,</span> <span class="n">DQNAlgorithmParameters</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.memories.episodic.episodic_experience_replay</span> <span class="k">import</span> <span class="n">EpisodicExperienceReplayParameters</span>
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<div class="viewcode-block" id="MixedMonteCarloAlgorithmParameters"><a class="viewcode-back" href="../../../components/agents/value_optimization/mmc.html#rl_coach.agents.mmc_agent.MixedMonteCarloAlgorithmParameters">[docs]</a><span class="k">class</span> <span class="nc">MixedMonteCarloAlgorithmParameters</span><span class="p">(</span><span class="n">DQNAlgorithmParameters</span><span class="p">):</span>
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<span class="sd">"""</span>
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<span class="sd"> :param monte_carlo_mixing_rate: (float)</span>
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<span class="sd"> The mixing rate is used for setting the amount of monte carlo estimate (full return) that will be mixes into</span>
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<span class="sd"> the single-step bootstrapped targets.</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">monte_carlo_mixing_rate</span> <span class="o">=</span> <span class="mf">0.1</span></div>
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<span class="k">class</span> <span class="nc">MixedMonteCarloAgentParameters</span><span class="p">(</span><span class="n">DQNAgentParameters</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">MixedMonteCarloAlgorithmParameters</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">EpisodicExperienceReplayParameters</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.mmc_agent:MixedMonteCarloAgent'</span>
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<span class="k">class</span> <span class="nc">MixedMonteCarloAgent</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">mixing_rate</span> <span class="o">=</span> <span class="n">agent_parameters</span><span class="o">.</span><span class="n">algorithm</span><span class="o">.</span><span class="n">monte_carlo_mixing_rate</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"># for the 1-step, we use the double-dqn target. hence actions are taken greedily according to the online network</span>
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<span class="n">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">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="mi">1</span><span class="p">)</span>
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<span class="c1"># TD_targets are initialized with the current prediction so that we will</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">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">total_returns</span> <span class="o">=</span> <span class="n">batch</span><span class="o">.</span><span class="n">n_step_discounted_rewards</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="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="n">one_step_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> \
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<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">monte_carlo_target</span> <span class="o">=</span> <span class="n">total_returns</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="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">mixing_rate</span><span class="p">)</span> <span class="o">*</span> <span class="n">one_step_target</span> <span class="o">+</span> \
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<span class="bp">self</span><span class="o">.</span><span class="n">mixing_rate</span> <span class="o">*</span> <span class="n">monte_carlo_target</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">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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</pre></div>
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