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

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* 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
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Gal Leibovich
2019-09-08 12:53:49 +03:00
committed by GitHub
parent fc50398544
commit 138ced23ba
46 changed files with 1193 additions and 51 deletions
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docs/_modules/rl_coach/memories/non_episodic/differentiable_neural_dictionary.html
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@@ -240,7 +240,7 @@
<span class="bp">self</span><span class="o">.</span><span class="n">built_capacity</span> <span class="o">=</span> <span class="mi">0</span>
<span class="k">def</span> <span class="nf">add</span><span class="p">(</span><span class="bp">self</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">additional_data</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="k">def</span> <span class="nf">add</span><span class="p">(</span><span class="bp">self</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">additional_data</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span> <span class="n">force_rebuild_tree</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">additional_data</span><span class="p">:</span>
<span class="n">additional_data</span> <span class="o">=</span> <span class="p">[</span><span class="kc">None</span><span class="p">]</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">keys</span><span class="p">)</span>
@@ -279,7 +279,7 @@
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">buffered_indices</span><span class="p">)</span> <span class="o">&gt;=</span> <span class="bp">self</span><span class="o">.</span><span class="n">min_update_size</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">min_update_size</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">initial_update_size</span><span class="p">,</span> <span class="nb">int</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">curr_size</span> <span class="o">*</span> <span class="mf">0.02</span><span class="p">))</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_rebuild_index</span><span class="p">()</span>
<span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">rebuild_on_every_update</span><span class="p">:</span>
<span class="k">elif</span> <span class="n">force_rebuild_tree</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">rebuild_on_every_update</span><span class="p">:</span>
<span class="bp">self</span><span class="o">.</span><span class="n">_rebuild_index</span><span class="p">()</span>
<span class="bp">self</span><span class="o">.</span><span class="n">current_timestamp</span> <span class="o">+=</span> <span class="mi">1</span>