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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
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parent fc50398544
commit 138ced23ba
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@@ -439,7 +439,7 @@ given observation</p>
<dl class="method">
<dt id="rl_coach.agents.dqn_agent.DQNAgent.prepare_batch_for_inference">
<code class="sig-name descname">prepare_batch_for_inference</code><span class="sig-paren">(</span><em class="sig-param">states: Union[Dict[str, numpy.ndarray], List[Dict[str, numpy.ndarray]]], network_name: str</em><span class="sig-paren">)</span> &#x2192; Dict[str, numpy.array]<a class="headerlink" href="#rl_coach.agents.dqn_agent.DQNAgent.prepare_batch_for_inference" title="Permalink to this definition"></a></dt>
<code class="sig-name descname">prepare_batch_for_inference</code><span class="sig-paren">(</span><em class="sig-param">states: Union[Dict[str, numpy.ndarray], List[Dict[str, numpy.ndarray]]], network_name: str</em><span class="sig-paren">)</span> &#x2192; Dict[str, numpy.core.multiarray.array]<a class="headerlink" href="#rl_coach.agents.dqn_agent.DQNAgent.prepare_batch_for_inference" title="Permalink to this definition"></a></dt>
<dd><p>Convert curr_state into input tensors tensorflow is expecting. i.e. if we have several inputs states, stack all
observations together, measurements together, etc.</p>
<dl class="field-list simple">