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<h1>Source code for rl_coach.agents.policy_gradients_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.policy_optimization_agent</span> <span class="k">import</span> <span class="n">PolicyOptimizationAgent</span><span class="p">,</span> <span class="n">PolicyGradientRescaler</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">PolicyHeadParameters</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">NetworkParameters</span><span class="p">,</span> <span class="n">AlgorithmParameters</span><span class="p">,</span> \
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<span class="n">AgentParameters</span>
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<span class="kn">from</span> <span class="nn">rl_coach.exploration_policies.additive_noise</span> <span class="k">import</span> <span class="n">AdditiveNoiseParameters</span>
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<span class="kn">from</span> <span class="nn">rl_coach.exploration_policies.categorical</span> <span class="k">import</span> <span class="n">CategoricalParameters</span>
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<span class="kn">from</span> <span class="nn">rl_coach.logger</span> <span class="k">import</span> <span class="n">screen</span>
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<span class="kn">from</span> <span class="nn">rl_coach.memories.episodic.single_episode_buffer</span> <span class="k">import</span> <span class="n">SingleEpisodeBufferParameters</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><span class="p">,</span> <span class="n">BoxActionSpace</span>
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<span class="k">class</span> <span class="nc">PolicyGradientNetworkParameters</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">PolicyHeadParameters</span><span class="p">()]</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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<div class="viewcode-block" id="PolicyGradientAlgorithmParameters"><a class="viewcode-back" href="../../../components/agents/policy_optimization/pg.html#rl_coach.agents.policy_gradients_agent.PolicyGradientAlgorithmParameters">[docs]</a><span class="k">class</span> <span class="nc">PolicyGradientAlgorithmParameters</span><span class="p">(</span><span class="n">AlgorithmParameters</span><span class="p">):</span>
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<span class="sd">"""</span>
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<span class="sd"> :param policy_gradient_rescaler: (PolicyGradientRescaler)</span>
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<span class="sd"> The rescaler type to use for the policy gradient loss. For policy gradients, we calculate log probability of</span>
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<span class="sd"> the action and then multiply it by the policy gradient rescaler. The most basic rescaler is the discounter</span>
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<span class="sd"> return, but there are other rescalers that are intended for reducing the variance of the updates.</span>
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<span class="sd"> :param apply_gradients_every_x_episodes: (int)</span>
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<span class="sd"> The number of episodes between applying the accumulated gradients to the network. After every</span>
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<span class="sd"> num_steps_between_gradient_updates steps, the agent will calculate the gradients for the collected data,</span>
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<span class="sd"> it will then accumulate it in internal accumulators, and will only apply them to the network once in every</span>
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<span class="sd"> apply_gradients_every_x_episodes episodes.</span>
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<span class="sd"> :param beta_entropy: (float)</span>
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<span class="sd"> A factor which defines the amount of entropy regularization to apply to the network. The entropy of the actions</span>
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<span class="sd"> will be added to the loss and scaled by the given beta factor.</span>
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<span class="sd"> :param num_steps_between_gradient_updates: (int)</span>
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<span class="sd"> The number of steps between calculating gradients for the collected data. In the A3C paper, this parameter is</span>
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<span class="sd"> called t_max. Since this algorithm is on-policy, only the steps collected between each two gradient calculations</span>
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<span class="sd"> are used in the batch.</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">policy_gradient_rescaler</span> <span class="o">=</span> <span class="n">PolicyGradientRescaler</span><span class="o">.</span><span class="n">FUTURE_RETURN_NORMALIZED_BY_TIMESTEP</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">apply_gradients_every_x_episodes</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">beta_entropy</span> <span class="o">=</span> <span class="mi">0</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">num_steps_between_gradient_updates</span> <span class="o">=</span> <span class="mi">20000</span> <span class="c1"># this is called t_max in all the papers</span></div>
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<span class="k">class</span> <span class="nc">PolicyGradientsAgentParameters</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">PolicyGradientAlgorithmParameters</span><span class="p">(),</span>
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<span class="n">exploration</span><span class="o">=</span><span class="p">{</span><span class="n">DiscreteActionSpace</span><span class="p">:</span> <span class="n">CategoricalParameters</span><span class="p">(),</span>
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<span class="n">BoxActionSpace</span><span class="p">:</span> <span class="n">AdditiveNoiseParameters</span><span class="p">()},</span>
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<span class="n">memory</span><span class="o">=</span><span class="n">SingleEpisodeBufferParameters</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">PolicyGradientNetworkParameters</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.policy_gradients_agent:PolicyGradientsAgent'</span>
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<span class="k">class</span> <span class="nc">PolicyGradientsAgent</span><span class="p">(</span><span class="n">PolicyOptimizationAgent</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">returns_mean</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="s1">'Returns Mean'</span><span class="p">)</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">returns_variance</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="s1">'Returns Variance'</span><span class="p">)</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">last_gradient_update_step_idx</span> <span class="o">=</span> <span class="mi">0</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="c1"># batch contains a list of episodes to learn from</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">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">reversed</span><span class="p">(</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="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">policy_gradient_rescaler</span> <span class="o">==</span> <span class="n">PolicyGradientRescaler</span><span class="o">.</span><span class="n">TOTAL_RETURN</span><span class="p">:</span>
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<span class="n">total_returns</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">total_returns</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
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<span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">policy_gradient_rescaler</span> <span class="o">==</span> <span class="n">PolicyGradientRescaler</span><span class="o">.</span><span class="n">FUTURE_RETURN</span><span class="p">:</span>
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<span class="c1"># just take the total return as it is</span>
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<span class="k">pass</span>
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<span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">policy_gradient_rescaler</span> <span class="o">==</span> <span class="n">PolicyGradientRescaler</span><span class="o">.</span><span class="n">FUTURE_RETURN_NORMALIZED_BY_EPISODE</span><span class="p">:</span>
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<span class="c1"># we can get a single transition episode while playing Doom Basic, causing the std to be 0</span>
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<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">std_discounted_return</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
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<span class="n">total_returns</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">total_returns</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">mean_discounted_return</span><span class="p">)</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">std_discounted_return</span>
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<span class="k">else</span><span class="p">:</span>
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<span class="n">total_returns</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
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<span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">policy_gradient_rescaler</span> <span class="o">==</span> <span class="n">PolicyGradientRescaler</span><span class="o">.</span><span class="n">FUTURE_RETURN_NORMALIZED_BY_TIMESTEP</span><span class="p">:</span>
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<span class="n">total_returns</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">mean_return_over_multiple_episodes</span><span class="p">[</span><span class="n">i</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">screen</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span><span class="s2">"WARNING: The requested policy gradient rescaler is not available"</span><span class="p">)</span>
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<span class="n">targets</span> <span class="o">=</span> <span class="n">total_returns</span>
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<span class="n">actions</span> <span class="o">=</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="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">DiscreteActionSpace</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="n">actions</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o"><</span> <span class="mi">2</span><span class="p">:</span>
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<span class="n">actions</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">actions</span><span class="p">,</span> <span class="o">-</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">returns_mean</span><span class="o">.</span><span class="n">add_sample</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">total_returns</span><span class="p">))</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">returns_variance</span><span class="o">.</span><span class="n">add_sample</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="n">total_returns</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">accumulate_gradients</span><span class="p">(</span>
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<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> <span class="s1">'output_0_0'</span><span class="p">:</span> <span class="n">actions</span><span class="p">},</span> <span class="n">targets</span>
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<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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