RL in Large Discrete Action Spaces - Wolpertinger Agent (#394)

* Currently this is specific to the case of discretizing a continuous action space. Can easily be adapted to other case by feeding the kNN otherwise, and removing the usage of a discretizing output action filter

docs/_modules/rl_coach/agents/agent.html

@@ -756,6 +756,9 @@
 
         <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">TEST</span><span class="p">:</span>
             <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">memory</span><span class="p">,</span> <span class="n">EpisodicExperienceReplay</span><span class="p">):</span>
+                <span class="k">if</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">override_episode_rewards_with_the_last_transition_reward</span><span class="p">:</span>
+                    <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">current_episode_buffer</span><span class="o">.</span><span class="n">transitions</span><span class="p">:</span>
+                        <span class="n">t</span><span class="o">.</span><span class="n">reward</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">current_episode_buffer</span><span class="o">.</span><span class="n">transitions</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">reward</span>
                 <span class="bp">self</span><span class="o">.</span><span class="n">call_memory</span><span class="p">(</span><span class="s1">&#39;store_episode&#39;</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">current_episode_buffer</span><span class="p">)</span>
             <span class="k">elif</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">store_transitions_only_when_episodes_are_terminated</span><span class="p">:</span>
                 <span class="k">for</span> <span class="n">transition</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">current_episode_buffer</span><span class="o">.</span><span class="n">transitions</span><span class="p">:</span>
@@ -910,7 +913,8 @@
                 <span class="c1"># update counters</span>
                 <span class="bp">self</span><span class="o">.</span><span class="n">training_iteration</span> <span class="o">+=</span> <span class="mi">1</span>
                 <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">pre_network_filter</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
-                    <span class="n">batch</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">pre_network_filter</span><span class="o">.</span><span class="n">filter</span><span class="p">(</span><span class="n">batch</span><span class="p">,</span> <span class="n">update_internal_state</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">deep_copy</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+                    <span class="n">update_internal_state</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">update_pre_network_filters_state_on_train</span>
+                    <span class="n">batch</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">pre_network_filter</span><span class="o">.</span><span class="n">filter</span><span class="p">(</span><span class="n">batch</span><span class="p">,</span> <span class="n">update_internal_state</span><span class="o">=</span><span class="n">update_internal_state</span><span class="p">,</span> <span class="n">deep_copy</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
 
                 <span class="c1"># if the batch returned empty then there are not enough samples in the replay buffer -&gt; skip</span>
                 <span class="c1"># training step</span>
@@ -1020,7 +1024,8 @@
                 <span class="c1"># informed action</span>
                 <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">pre_network_filter</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
                     <span class="c1"># before choosing an action, first use the pre_network_filter to filter out the current state</span>
-                    <span class="n">update_filter_internal_state</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">phase</span> <span class="ow">is</span> <span class="ow">not</span> <span class="n">RunPhase</span><span class="o">.</span><span class="n">TEST</span>
+                    <span class="n">update_filter_internal_state</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">update_pre_network_filters_state_on_inference</span> <span class="ow">and</span> \
+                                                   <span class="bp">self</span><span class="o">.</span><span class="n">phase</span> <span class="ow">is</span> <span class="ow">not</span> <span class="n">RunPhase</span><span class="o">.</span><span class="n">TEST</span>
                     <span class="n">curr_state</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">run_pre_network_filter_for_inference</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">curr_state</span><span class="p">,</span> <span class="n">update_filter_internal_state</span><span class="p">)</span>
 
                 <span class="k">else</span><span class="p">:</span>
@@ -1048,6 +1053,10 @@
 <span class="sd">        :return: The filtered state</span>
 <span class="sd">        &quot;&quot;&quot;</span>
         <span class="n">dummy_env_response</span> <span class="o">=</span> <span class="n">EnvResponse</span><span class="p">(</span><span class="n">next_state</span><span class="o">=</span><span class="n">state</span><span class="p">,</span> <span class="n">reward</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">game_over</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+
+        <span class="c1"># TODO actually we only want to run the observation filters. No point in running the reward filters as the</span>
+        <span class="c1">#  filtered reward is being ignored anyway (and it might unncecessarily affect the reward filters&#39; internal</span>
+        <span class="c1">#  state).</span>
         <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">pre_network_filter</span><span class="o">.</span><span class="n">filter</span><span class="p">(</span><span class="n">dummy_env_response</span><span class="p">,</span>
                                               <span class="n">update_internal_state</span><span class="o">=</span><span class="n">update_filter_internal_state</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">next_state</span></div>
 
@@ -1177,7 +1186,7 @@
         <span class="sd">&quot;&quot;&quot;</span>
 <span class="sd">        Allows setting a directive for the agent to follow. This is useful in hierarchy structures, where the agent</span>
 <span class="sd">        has another master agent that is controlling it. In such cases, the master agent can define the goals for the</span>
-<span class="sd">        slave agent, define it&#39;s observation, possible actions, etc. The directive type is defined by the agent</span>
+<span class="sd">        slave agent, define its observation, possible actions, etc. The directive type is defined by the agent</span>
 <span class="sd">        in-action-space.</span>
 
 <span class="sd">        :param action: The action that should be set as the directive</span>

docs/_modules/rl_coach/agents/clipped_ppo_agent.html

@@ -295,7 +295,9 @@
         <span class="bp">self</span><span class="o">.</span><span class="n">optimization_epochs</span> <span class="o">=</span> <span class="mi">10</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">normalization_stats</span> <span class="o">=</span> <span class="kc">None</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">clipping_decay_schedule</span> <span class="o">=</span> <span class="n">ConstantSchedule</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">act_for_full_episodes</span> <span class="o">=</span> <span class="kc">True</span></div>
+        <span class="bp">self</span><span class="o">.</span><span class="n">act_for_full_episodes</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">update_pre_network_filters_state_on_train</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">update_pre_network_filters_state_on_inference</span> <span class="o">=</span> <span class="kc">False</span></div>
 
 
 <span class="k">class</span> <span class="nc">ClippedPPOAgentParameters</span><span class="p">(</span><span class="n">AgentParameters</span><span class="p">):</span>
@@ -486,7 +488,9 @@
                 <span class="n">network</span><span class="o">.</span><span class="n">set_is_training</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
 
             <span class="n">dataset</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">memory</span><span class="o">.</span><span class="n">transitions</span>
-            <span class="n">dataset</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">pre_network_filter</span><span class="o">.</span><span class="n">filter</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">deep_copy</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+            <span class="n">update_internal_state</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">update_pre_network_filters_state_on_train</span>
+            <span class="n">dataset</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">pre_network_filter</span><span class="o">.</span><span class="n">filter</span><span class="p">(</span><span class="n">dataset</span><span class="p">,</span> <span class="n">deep_copy</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+                                                     <span class="n">update_internal_state</span><span class="o">=</span><span class="n">update_internal_state</span><span class="p">)</span>
             <span class="n">batch</span> <span class="o">=</span> <span class="n">Batch</span><span class="p">(</span><span class="n">dataset</span><span class="p">)</span>
 
             <span class="k">for</span> <span class="n">training_step</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">algorithm</span><span class="o">.</span><span class="n">num_consecutive_training_steps</span><span class="p">):</span>
@@ -512,7 +516,9 @@
 
     <span class="k">def</span> <span class="nf">run_pre_network_filter_for_inference</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">state</span><span class="p">:</span> <span class="n">StateType</span><span class="p">,</span> <span class="n">update_internal_state</span><span class="p">:</span> <span class="nb">bool</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
         <span class="n">dummy_env_response</span> <span class="o">=</span> <span class="n">EnvResponse</span><span class="p">(</span><span class="n">next_state</span><span class="o">=</span><span class="n">state</span><span class="p">,</span> <span class="n">reward</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">game_over</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">pre_network_filter</span><span class="o">.</span><span class="n">filter</span><span class="p">(</span><span class="n">dummy_env_response</span><span class="p">,</span> <span class="n">update_internal_state</span><span class="o">=</span><span class="kc">False</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">next_state</span>
+        <span class="n">update_internal_state</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">update_pre_network_filters_state_on_inference</span>
+        <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">pre_network_filter</span><span class="o">.</span><span class="n">filter</span><span class="p">(</span>
+            <span class="n">dummy_env_response</span><span class="p">,</span> <span class="n">update_internal_state</span><span class="o">=</span><span class="n">update_internal_state</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">next_state</span>
 
     <span class="k">def</span> <span class="nf">choose_action</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">curr_state</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">clipping_decay_schedule</span><span class="o">.</span><span class="n">step</span><span class="p">()</span>

docs/_modules/rl_coach/agents/wolpertinger_agent.html

@@ -0,0 +1,356 @@
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+            
+  <h1>Source code for rl_coach.agents.wolpertinger_agent</h1><div class="highlight"><pre>
+<span></span><span class="c1">#</span>
+<span class="c1"># Copyright (c) 2019 Intel Corporation </span>
+<span class="c1">#</span>
+<span class="c1"># Licensed under the Apache License, Version 2.0 (the &quot;License&quot;);</span>
+<span class="c1"># you may not use this file except in compliance with the License.</span>
+<span class="c1"># You may obtain a copy of the License at</span>
+<span class="c1">#</span>
+<span class="c1">#      http://www.apache.org/licenses/LICENSE-2.0</span>
+<span class="c1">#</span>
+<span class="c1"># Unless required by applicable law or agreed to in writing, software</span>
+<span class="c1"># distributed under the License is distributed on an &quot;AS IS&quot; BASIS,</span>
+<span class="c1"># WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.</span>
+<span class="c1"># See the License for the specific language governing permissions and</span>
+<span class="c1"># limitations under the License.</span>
+<span class="c1">#</span>
+
+<span class="kn">import</span> <span class="nn">copy</span>
+<span class="kn">from</span> <span class="nn">typing</span> <span class="k">import</span> <span class="n">Union</span>
+<span class="kn">from</span> <span class="nn">collections</span> <span class="k">import</span> <span class="n">OrderedDict</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+
+<span class="kn">from</span> <span class="nn">rl_coach.agents.ddpg_agent</span> <span class="k">import</span> <span class="n">DDPGAlgorithmParameters</span><span class="p">,</span> <span class="n">DDPGActorNetworkParameters</span><span class="p">,</span> \
+    <span class="n">DDPGCriticNetworkParameters</span><span class="p">,</span> <span class="n">DDPGAgent</span>
+<span class="kn">from</span> <span class="nn">rl_coach.base_parameters</span> <span class="k">import</span> <span class="n">AgentParameters</span>
+<span class="kn">from</span> <span class="nn">rl_coach.core_types</span> <span class="k">import</span> <span class="n">ActionInfo</span>
+<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>
+<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>
+<span class="kn">from</span> <span class="nn">rl_coach.memories.non_episodic.differentiable_neural_dictionary</span> <span class="k">import</span> <span class="n">AnnoyDictionary</span>
+<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>
+<span class="kn">from</span> <span class="nn">rl_coach.architectures.head_parameters</span> <span class="k">import</span> <span class="n">WolpertingerActorHeadParameters</span>
+
+
+<span class="k">class</span> <span class="nc">WolpertingerCriticNetworkParameters</span><span class="p">(</span><span class="n">DDPGCriticNetworkParameters</span><span class="p">):</span>
+    <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">use_batchnorm</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
+        <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">use_batchnorm</span><span class="o">=</span><span class="n">use_batchnorm</span><span class="p">)</span>
+
+
+<span class="k">class</span> <span class="nc">WolpertingerActorNetworkParameters</span><span class="p">(</span><span class="n">DDPGActorNetworkParameters</span><span class="p">):</span>
+    <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">use_batchnorm</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
+        <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">WolpertingerActorHeadParameters</span><span class="p">(</span><span class="n">batchnorm</span><span class="o">=</span><span class="n">use_batchnorm</span><span class="p">)]</span>
+
+
+<div class="viewcode-block" id="WolpertingerAlgorithmParameters"><a class="viewcode-back" href="../../../components/agents/policy_optimization/wolpertinger.html#rl_coach.agents.wolpertinger_agent.WolpertingerAlgorithmParameters">[docs]</a><span class="k">class</span> <span class="nc">WolpertingerAlgorithmParameters</span><span class="p">(</span><span class="n">DDPGAlgorithmParameters</span><span class="p">):</span>
+    <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="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">action_embedding_width</span> <span class="o">=</span> <span class="mi">1</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">k</span> <span class="o">=</span> <span class="mi">1</span></div>
+        
+
+<span class="k">class</span> <span class="nc">WolpertingerAgentParameters</span><span class="p">(</span><span class="n">AgentParameters</span><span class="p">):</span>
+    <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">use_batchnorm</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
+        <span class="n">exploration_params</span> <span class="o">=</span> <span class="n">AdditiveNoiseParameters</span><span class="p">()</span>
+        <span class="n">exploration_params</span><span class="o">.</span><span class="n">noise_as_percentage_from_action_space</span> <span class="o">=</span> <span class="kc">False</span>
+
+        <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">WolpertingerAlgorithmParameters</span><span class="p">(),</span>
+                         <span class="n">exploration</span><span class="o">=</span><span class="n">exploration_params</span><span class="p">,</span>
+                         <span class="n">memory</span><span class="o">=</span><span class="n">EpisodicExperienceReplayParameters</span><span class="p">(),</span>
+                         <span class="n">networks</span><span class="o">=</span><span class="n">OrderedDict</span><span class="p">(</span>
+                             <span class="p">[(</span><span class="s2">&quot;actor&quot;</span><span class="p">,</span> <span class="n">WolpertingerActorNetworkParameters</span><span class="p">(</span><span class="n">use_batchnorm</span><span class="o">=</span><span class="n">use_batchnorm</span><span class="p">)),</span>
+                              <span class="p">(</span><span class="s2">&quot;critic&quot;</span><span class="p">,</span> <span class="n">WolpertingerCriticNetworkParameters</span><span class="p">(</span><span class="n">use_batchnorm</span><span class="o">=</span><span class="n">use_batchnorm</span><span class="p">))]))</span>
+
+    <span class="nd">@property</span>
+    <span class="k">def</span> <span class="nf">path</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="s1">&#39;rl_coach.agents.wolpertinger_agent:WolpertingerAgent&#39;</span>
+
+
+<span class="c1"># Deep Reinforcement Learning in Large Discrete Action Spaces - https://arxiv.org/pdf/1512.07679.pdf</span>
+<span class="k">class</span> <span class="nc">WolpertingerAgent</span><span class="p">(</span><span class="n">DDPGAgent</span><span class="p">):</span>
+    <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">&#39;LevelManager&#39;</span><span class="p">,</span> <span class="s1">&#39;CompositeAgent&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">):</span>
+        <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>
+
+    <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>
+        <span class="c1"># replay buffer holds the actions in the discrete manner, as the agent is expected to act with discrete actions</span>
+        <span class="c1"># with the BoxDiscretization output filter. But DDPG needs to work on continuous actions, thus converting to</span>
+        <span class="c1"># continuous actions. This is actually a duplicate since this filtering is also done before applying actions on</span>
+        <span class="c1"># the environment. So might want to somehow reuse that conversion. Maybe can hold this information in the info</span>
+        <span class="c1"># dictionary of the transition.</span>
+
+        <span class="n">output_action_filter</span> <span class="o">=</span> \
+            <span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">output_filter</span><span class="o">.</span><span class="n">action_filters</span><span class="o">.</span><span class="n">values</span><span class="p">())[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="n">continuous_actions</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">action</span> <span class="ow">in</span> <span class="n">batch</span><span class="o">.</span><span class="n">actions</span><span class="p">():</span>
+            <span class="n">continuous_actions</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">output_action_filter</span><span class="o">.</span><span class="n">filter</span><span class="p">(</span><span class="n">action</span><span class="p">))</span>
+        <span class="n">batch</span><span class="o">.</span><span class="n">_actions</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="n">continuous_actions</span><span class="p">)</span><span class="o">.</span><span class="n">squeeze</span><span class="p">()</span>
+
+        <span class="k">return</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">learn_from_batch</span><span class="p">(</span><span class="n">batch</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">train</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="k">return</span> <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">train</span><span class="p">()</span>
+
+    <span class="k">def</span> <span class="nf">choose_action</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">curr_state</span><span class="p">):</span>
+        <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</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="n">DiscreteActionSpace</span><span class="p">):</span>
+            <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">&quot;WolpertingerAgent works only for discrete control problems&quot;</span><span class="p">)</span>
+
+        <span class="c1"># convert to batch so we can run it through the network</span>
+        <span class="n">tf_input_state</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">prepare_batch_for_inference</span><span class="p">(</span><span class="n">curr_state</span><span class="p">,</span> <span class="s1">&#39;actor&#39;</span><span class="p">)</span>
+        <span class="n">actor_network</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">&#39;actor&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">online_network</span>
+        <span class="n">critic_network</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">&#39;critic&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">online_network</span>
+        <span class="n">proto_action</span> <span class="o">=</span> <span class="n">actor_network</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">tf_input_state</span><span class="p">)</span>
+        <span class="n">proto_action</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="bp">self</span><span class="o">.</span><span class="n">exploration_policy</span><span class="o">.</span><span class="n">get_action</span><span class="p">(</span><span class="n">proto_action</span><span class="p">),</span> <span class="mi">0</span><span class="p">)</span>
+
+        <span class="n">nn_action_embeddings</span><span class="p">,</span> <span class="n">indices</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">knn_tree</span><span class="o">.</span><span class="n">query</span><span class="p">(</span><span class="n">keys</span><span class="o">=</span><span class="n">proto_action</span><span class="p">,</span> <span class="n">k</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">k</span><span class="p">)</span>
+
+        <span class="c1"># now move the actions through the critic and choose the one with the highest q value</span>
+        <span class="n">critic_inputs</span> <span class="o">=</span> <span class="n">copy</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">tf_input_state</span><span class="p">)</span>
+        <span class="n">critic_inputs</span><span class="p">[</span><span class="s1">&#39;observation&#39;</span><span class="p">]</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">critic_inputs</span><span class="p">[</span><span class="s1">&#39;observation&#39;</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">algorithm</span><span class="o">.</span><span class="n">k</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
+        <span class="n">critic_inputs</span><span class="p">[</span><span class="s1">&#39;action&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">nn_action_embeddings</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="n">q_values</span> <span class="o">=</span> <span class="n">critic_network</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">critic_inputs</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="n">action</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">indices</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">q_values</span><span class="p">)])</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">action_signal</span><span class="o">.</span><span class="n">add_sample</span><span class="p">(</span><span class="n">action</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">ActionInfo</span><span class="p">(</span><span class="n">action</span><span class="o">=</span><span class="n">action</span><span class="p">,</span> <span class="n">action_value</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">init_environment_dependent_modules</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">init_environment_dependent_modules</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">knn_tree</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_initialized_knn</span><span class="p">()</span>
+
+    <span class="c1"># TODO - ideally the knn should not be defined here, but somehow be defined by the user in the preset</span>
+    <span class="k">def</span> <span class="nf">get_initialized_knn</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+        <span class="n">num_actions</span> <span class="o">=</span> <span class="nb">len</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="o">.</span><span class="n">actions</span><span class="p">)</span>
+        <span class="n">action_max_abs_range</span> <span class="o">=</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="o">.</span><span class="n">filtered_action_space</span><span class="o">.</span><span class="n">max_abs_range</span> <span class="k">if</span> \
+            <span class="p">(</span><span class="nb">hasattr</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="s1">&#39;filtered_action_space&#39;</span><span class="p">)</span> <span class="ow">and</span>
+             <span class="nb">isinstance</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="o">.</span><span class="n">filtered_action_space</span><span class="p">,</span> <span class="n">BoxActionSpace</span><span class="p">))</span> \
+            <span class="k">else</span> <span class="mf">1.0</span>
+        <span class="n">keys</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">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">num_actions</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">num_actions</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">-</span> <span class="mf">0.5</span><span class="p">)</span> <span class="o">*</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="n">action_max_abs_range</span>
+        <span class="n">values</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">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">num_actions</span><span class="p">),</span> <span class="mi">1</span><span class="p">)</span>
+        <span class="n">knn_tree</span> <span class="o">=</span> <span class="n">AnnoyDictionary</span><span class="p">(</span><span class="n">dict_size</span><span class="o">=</span><span class="n">num_actions</span><span class="p">,</span> <span class="n">key_width</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">action_embedding_width</span><span class="p">)</span>
+        <span class="n">knn_tree</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">keys</span><span class="p">,</span> <span class="n">values</span><span class="p">,</span> <span class="n">force_rebuild_tree</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">knn_tree</span>
+
+</pre></div>
+
+           </div>
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