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<h1>Source code for rl_coach.exploration_policies.bootstrapped</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">List</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.core_types</span> <span class="k">import</span> <span class="n">RunPhase</span><span class="p">,</span> <span class="n">ActionType</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.e_greedy</span> <span class="k">import</span> <span class="n">EGreedy</span><span class="p">,</span> <span class="n">EGreedyParameters</span>
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<span class="kn">from</span> <span class="nn">rl_coach.exploration_policies.exploration_policy</span> <span class="k">import</span> <span class="n">ExplorationParameters</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">Schedule</span><span class="p">,</span> <span class="n">LinearSchedule</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">ActionSpace</span>
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<span class="k">class</span> <span class="nc">BootstrappedParameters</span><span class="p">(</span><span class="n">EGreedyParameters</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">architecture_num_q_heads</span> <span class="o">=</span> <span class="mi">10</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">bootstrapped_data_sharing_probability</span> <span class="o">=</span> <span class="mf">1.0</span>
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<span class="bp">self</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">0.01</span><span class="p">,</span> <span class="mi">1000000</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.exploration_policies.bootstrapped:Bootstrapped'</span>
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<div class="viewcode-block" id="Bootstrapped"><a class="viewcode-back" href="../../../components/exploration_policies/index.html#rl_coach.exploration_policies.bootstrapped.Bootstrapped">[docs]</a><span class="k">class</span> <span class="nc">Bootstrapped</span><span class="p">(</span><span class="n">EGreedy</span><span class="p">):</span>
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<span class="sd">"""</span>
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<span class="sd"> Bootstrapped exploration policy is currently only used for discrete action spaces along with the</span>
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<span class="sd"> Bootstrapped DQN agent. It assumes that there is an ensemble of network heads, where each one predicts the</span>
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<span class="sd"> values for all the possible actions. For each episode, a single head is selected to lead the agent, according</span>
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<span class="sd"> to its value predictions. In evaluation, the action is selected using a majority vote over all the heads</span>
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<span class="sd"> predictions.</span>
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<span class="sd"> .. note::</span>
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<span class="sd"> This exploration policy will only work for Discrete action spaces with Bootstrapped DQN style agents,</span>
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<span class="sd"> since it requires the agent to have a network with multiple heads.</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> <span class="n">action_space</span><span class="p">:</span> <span class="n">ActionSpace</span><span class="p">,</span> <span class="n">epsilon_schedule</span><span class="p">:</span> <span class="n">Schedule</span><span class="p">,</span> <span class="n">evaluation_epsilon</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span>
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<span class="n">architecture_num_q_heads</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
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<span class="n">continuous_exploration_policy_parameters</span><span class="p">:</span> <span class="n">ExplorationParameters</span> <span class="o">=</span> <span class="n">AdditiveNoiseParameters</span><span class="p">(),):</span>
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<span class="sd">"""</span>
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<span class="sd"> :param action_space: the action space used by the environment</span>
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<span class="sd"> :param epsilon_schedule: a schedule for the epsilon values</span>
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<span class="sd"> :param evaluation_epsilon: the epsilon value to use for evaluation phases</span>
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<span class="sd"> :param continuous_exploration_policy_parameters: the parameters of the continuous exploration policy to use</span>
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<span class="sd"> if the e-greedy is used for a continuous policy</span>
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<span class="sd"> :param architecture_num_q_heads: the number of q heads to select from</span>
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<span class="sd"> """</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">action_space</span><span class="p">,</span> <span class="n">epsilon_schedule</span><span class="p">,</span> <span class="n">evaluation_epsilon</span><span class="p">,</span> <span class="n">continuous_exploration_policy_parameters</span><span class="p">)</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">num_heads</span> <span class="o">=</span> <span class="n">architecture_num_q_heads</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">selected_head</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">last_action_values</span> <span class="o">=</span> <span class="mi">0</span>
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<span class="k">def</span> <span class="nf">select_head</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">selected_head</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">num_heads</span><span class="p">)</span>
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<span class="k">def</span> <span class="nf">get_action</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">action_values</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">ActionType</span><span class="p">])</span> <span class="o">-></span> <span class="n">ActionType</span><span class="p">:</span>
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<span class="c1"># action values are none in case the exploration policy is going to select a random action</span>
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<span class="k">if</span> <span class="n">action_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="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">phase</span> <span class="o">==</span> <span class="n">RunPhase</span><span class="o">.</span><span class="n">TRAIN</span><span class="p">:</span>
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<span class="n">action_values</span> <span class="o">=</span> <span class="n">action_values</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">selected_head</span><span class="p">]</span>
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<span class="k">else</span><span class="p">:</span>
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<span class="c1"># ensemble voting for evaluation</span>
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<span class="n">top_action_votings</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="n">action_values</span><span class="p">,</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
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<span class="n">counts</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">bincount</span><span class="p">(</span><span class="n">top_action_votings</span><span class="o">.</span><span class="n">squeeze</span><span class="p">())</span>
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<span class="n">top_action</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="n">counts</span><span class="p">)</span>
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<span class="c1"># convert the top action to a one hot vector and pass it to e-greedy</span>
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<span class="n">action_values</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">eye</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">action_space</span><span class="o">.</span><span class="n">actions</span><span class="p">))[[</span><span class="n">top_action</span><span class="p">]]</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">last_action_values</span> <span class="o">=</span> <span class="n">action_values</span>
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<span class="k">return</span> <span class="nb">super</span><span class="p">()</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="k">def</span> <span class="nf">get_control_param</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="bp">self</span><span class="o">.</span><span class="n">selected_head</span></div>
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