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main/.buildinfo

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 # Sphinx build info version 1
 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
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+config: 0a9a654f36eb6815b4af662934eeb931
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main/_modules/pettingzoo/mpe/simple_adversary/simple_adversary/index.html

@@ -420,7 +420,7 @@ <h1>Source code for pettingzoo.mpe.simple_adversary.simple_adversary</h1><div cl
 <span class="sd">target landmark, but negatively rewarded based on how close the adversary is to the target landmark. The adversary is rewarded based on distance to the target, but it doesn&#39;t know which landmark is the target landmark. All rewards are unscaled Euclidean distance (see main MPE documentation for</span>
 <span class="sd">average distance). This means good agents have to learn to &#39;split up&#39; and cover all landmarks to deceive the adversary.</span>
 
-<span class="sd">Agent observation space: `[self_pos, self_vel, goal_rel_position, landmark_rel_position, other_agent_rel_positions]`</span>
+<span class="sd">Agent observation space: `[goal_rel_position, landmark_rel_position, other_agent_rel_positions]`</span>
 
 <span class="sd">Adversary observation space: `[landmark_rel_position, other_agents_rel_positions]`</span>
 

main/environments/mpe/simple_adversary/index.html

@@ -445,7 +445,7 @@ <h1>Simple Adversary<a class="headerlink" href="#simple-adversary" title="Link t
 <p>In this environment, there is 1 adversary (red), N good agents (green), N landmarks (default N=2). All agents observe the position of landmarks and other agents. One landmark is the ‘target landmark’ (colored green). Good agents are rewarded based on how close the closest one of them is to the
 target landmark, but negatively rewarded based on how close the adversary is to the target landmark. The adversary is rewarded based on distance to the target, but it doesn’t know which landmark is the target landmark. All rewards are unscaled Euclidean distance (see main MPE documentation for
 average distance). This means good agents have to learn to ‘split up’ and cover all landmarks to deceive the adversary.</p>
-<p>Agent observation space: <code class="docutils literal notranslate"><span class="pre">[self_pos,</span> <span class="pre">self_vel,</span> <span class="pre">goal_rel_position,</span> <span class="pre">landmark_rel_position,</span> <span class="pre">other_agent_rel_positions]</span></code></p>
+<p>Agent observation space: <code class="docutils literal notranslate"><span class="pre">[goal_rel_position,</span> <span class="pre">landmark_rel_position,</span> <span class="pre">other_agent_rel_positions]</span></code></p>
 <p>Adversary observation space: <code class="docutils literal notranslate"><span class="pre">[landmark_rel_position,</span> <span class="pre">other_agents_rel_positions]</span></code></p>
 <p>Agent action space: <code class="docutils literal notranslate"><span class="pre">[no_action,</span> <span class="pre">move_left,</span> <span class="pre">move_right,</span> <span class="pre">move_down,</span> <span class="pre">move_up]</span></code></p>
 <p>Adversary action space: <code class="docutils literal notranslate"><span class="pre">[no_action,</span> <span class="pre">move_left,</span> <span class="pre">move_right,</span> <span class="pre">move_down,</span> <span class="pre">move_up]</span></code></p>