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<h1>Source code for rl_coach.agents.qr_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.dqn_agent</span> <span class="k">import</span> <span class="n">DQNAgentParameters</span><span class="p">,</span> <span class="n">DQNNetworkParameters</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.architectures.head_parameters</span> <span class="k">import</span> <span class="n">QuantileRegressionQHeadParameters</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">StateType</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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<span class="k">class</span> <span class="nc">QuantileRegressionDQNNetworkParameters</span><span class="p">(</span><span class="n">DQNNetworkParameters</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">heads_parameters</span> <span class="o">=</span> <span class="p">[</span><span class="n">QuantileRegressionQHeadParameters</span><span class="p">()]</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">0.00005</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">optimizer_epsilon</span> <span class="o">=</span> <span class="mf">0.01</span> <span class="o">/</span> <span class="mi">32</span>
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<div class="viewcode-block" id="QuantileRegressionDQNAlgorithmParameters"><a class="viewcode-back" href="../../../components/agents/value_optimization/qr_dqn.html#rl_coach.agents.qr_dqn_agent.QuantileRegressionDQNAlgorithmParameters">[docs]</a><span class="k">class</span> <span class="nc">QuantileRegressionDQNAlgorithmParameters</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 atoms: (int)</span>
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<span class="sd"> the number of atoms to predict for each action</span>
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<span class="sd"> :param huber_loss_interval: (float)</span>
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<span class="sd"> One of the huber loss parameters, and is referred to as :math:`\kapa` in the paper.</span>
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<span class="sd"> It describes the interval [-k, k] in which the huber loss acts as a MSE loss.</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">atoms</span> <span class="o">=</span> <span class="mi">200</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">huber_loss_interval</span> <span class="o">=</span> <span class="mi">1</span> <span class="c1"># called k in the paper</span></div>
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<span class="k">class</span> <span class="nc">QuantileRegressionDQNAgentParameters</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">QuantileRegressionDQNAlgorithmParameters</span><span class="p">()</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">network_wrappers</span> <span class="o">=</span> <span class="p">{</span><span class="s2">"main"</span><span class="p">:</span> <span class="n">QuantileRegressionDQNNetworkParameters</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">0.01</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">0.001</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.qr_dqn_agent:QuantileRegressionDQNAgent'</span>
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<span class="c1"># Quantile Regression Deep Q Network - https://arxiv.org/pdf/1710.10044v1.pdf</span>
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<span class="k">class</span> <span class="nc">QuantileRegressionDQNAgent</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">quantile_probabilities</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</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">algorithm</span><span class="o">.</span><span class="n">atoms</span><span class="p">)</span> <span class="o">/</span> <span class="nb">float</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">algorithm</span><span class="o">.</span><span class="n">atoms</span><span class="p">)</span>
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<span class="k">def</span> <span class="nf">get_q_values</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">quantile_values</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">dot</span><span class="p">(</span><span class="n">quantile_values</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">quantile_probabilities</span><span class="p">)</span>
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<span class="c1"># prediction's format is (batch,actions,atoms)</span>
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<span class="k">def</span> <span class="nf">get_all_q_values_for_states</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">states</span><span class="p">:</span> <span class="n">StateType</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">exploration_policy</span><span class="o">.</span><span class="n">requires_action_values</span><span class="p">():</span>
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<span class="n">quantile_values</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_prediction</span><span class="p">(</span><span class="n">states</span><span class="p">)</span>
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<span class="n">actions_q_values</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_q_values</span><span class="p">(</span><span class="n">quantile_values</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">actions_q_values</span> <span class="o">=</span> <span class="kc">None</span>
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<span class="k">return</span> <span class="n">actions_q_values</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"># get the quantiles of the next states and current states</span>
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<span class="n">next_state_quantiles</span><span class="p">,</span> <span class="n">current_quantiles</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="c1"># get the optimal actions to take for the next states</span>
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<span class="n">target_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">get_q_values</span><span class="p">(</span><span class="n">next_state_quantiles</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="c1"># calculate the Bellman update</span>
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<span class="n">batch_idx</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</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">TD_targets</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="kc">True</span><span class="p">)</span> <span class="o">+</span> <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="kc">True</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> \
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<span class="o">*</span> <span class="n">next_state_quantiles</span><span class="p">[</span><span class="n">batch_idx</span><span class="p">,</span> <span class="n">target_actions</span><span class="p">]</span>
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<span class="c1"># get the locations of the selected actions within the batch for indexing purposes</span>
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<span class="n">actions_locations</span> <span class="o">=</span> <span class="p">[[</span><span class="n">b</span><span class="p">,</span> <span class="n">a</span><span class="p">]</span> <span class="k">for</span> <span class="n">b</span><span class="p">,</span> <span class="n">a</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">batch_idx</span><span class="p">,</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="c1"># calculate the cumulative quantile probabilities and reorder them to fit the sorted quantiles order</span>
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<span class="n">cumulative_probabilities</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</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">algorithm</span><span class="o">.</span><span class="n">atoms</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span> <span class="o">/</span> <span class="nb">float</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">algorithm</span><span class="o">.</span><span class="n">atoms</span><span class="p">)</span> <span class="c1"># tau_i</span>
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<span class="n">quantile_midpoints</span> <span class="o">=</span> <span class="mf">0.5</span><span class="o">*</span><span class="p">(</span><span class="n">cumulative_probabilities</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span> <span class="o">+</span> <span class="n">cumulative_probabilities</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span> <span class="c1"># tau^hat_i</span>
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<span class="n">quantile_midpoints</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">tile</span><span class="p">(</span><span class="n">quantile_midpoints</span><span class="p">,</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> <span class="mi">1</span><span class="p">))</span>
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<span class="n">sorted_quantiles</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argsort</span><span class="p">(</span><span class="n">current_quantiles</span><span class="p">[</span><span class="n">batch_idx</span><span class="p">,</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">for</span> <span class="n">idx</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">quantile_midpoints</span><span class="p">[</span><span class="n">idx</span><span class="p">,</span> <span class="p">:]</span> <span class="o">=</span> <span class="n">quantile_midpoints</span><span class="p">[</span><span class="n">idx</span><span class="p">,</span> <span class="n">sorted_quantiles</span><span class="p">[</span><span class="n">idx</span><span class="p">]]</span>
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<span class="c1"># train</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>
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|
<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">actions_locations</span><span class="p">,</span>
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<span class="s1">'output_0_1'</span><span class="p">:</span> <span class="n">quantile_midpoints</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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</pre></div>
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