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* SAC algorithm * SAC - updates to agent (learn_from_batch), sac_head and sac_q_head to fix problem in gradient calculation. Now SAC agents is able to train. gym_environment - fixing an error in access to gym.spaces * Soft Actor Critic - code cleanup * code cleanup * V-head initialization fix * SAC benchmarks * SAC Documentation * typo fix * documentation fixes * documentation and version update * README typo
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<div class="section" id="neural-episodic-control">
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<h1>Neural Episodic Control<a class="headerlink" href="#neural-episodic-control" title="Permalink to this headline">¶</a></h1>
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<p><strong>Actions space:</strong> Discrete</p>
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<p><strong>References:</strong> <a class="reference external" href="https://arxiv.org/abs/1703.01988">Neural Episodic Control</a></p>
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<div class="section" id="network-structure">
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<h2>Network Structure<a class="headerlink" href="#network-structure" title="Permalink to this headline">¶</a></h2>
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<a class="reference internal image-reference" href="../../../_images/nec.png"><img alt="../../../_images/nec.png" class="align-center" src="../../../_images/nec.png" style="width: 500px;" /></a>
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</div>
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<div class="section" id="algorithm-description">
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<h2>Algorithm Description<a class="headerlink" href="#algorithm-description" title="Permalink to this headline">¶</a></h2>
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<div class="section" id="choosing-an-action">
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<h3>Choosing an action<a class="headerlink" href="#choosing-an-action" title="Permalink to this headline">¶</a></h3>
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<ol class="arabic simple">
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<li>Use the current state as an input to the online network and extract the state embedding, which is the intermediate
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output from the middleware.</li>
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<li>For each possible action <span class="math notranslate nohighlight">\(a_i\)</span>, run the DND head using the state embedding and the selected action <span class="math notranslate nohighlight">\(a_i\)</span> as inputs.
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The DND is queried and returns the <span class="math notranslate nohighlight">\(P\)</span> nearest neighbor keys and values. The keys and values are used to calculate
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and return the action <span class="math notranslate nohighlight">\(Q\)</span> value from the network.</li>
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<li>Pass all the <span class="math notranslate nohighlight">\(Q\)</span> values to the exploration policy and choose an action accordingly.</li>
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<li>Store the state embeddings and actions taken during the current episode in a small buffer <span class="math notranslate nohighlight">\(B\)</span>, in order to
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accumulate transitions until it is possible to calculate the total discounted returns over the entire episode.</li>
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</ol>
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</div>
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<div class="section" id="finalizing-an-episode">
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<h3>Finalizing an episode<a class="headerlink" href="#finalizing-an-episode" title="Permalink to this headline">¶</a></h3>
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<p>For each step in the episode, the state embeddings and the taken actions are stored in the buffer <span class="math notranslate nohighlight">\(B\)</span>.
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When the episode is finished, the replay buffer calculates the <span class="math notranslate nohighlight">\(N\)</span>-step total return of each transition in the
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buffer, bootstrapped using the maximum <span class="math notranslate nohighlight">\(Q\)</span> value of the <span class="math notranslate nohighlight">\(N\)</span>-th transition. Those values are inserted
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along with the total return into the DND, and the buffer <span class="math notranslate nohighlight">\(B\)</span> is reset.</p>
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</div>
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<div class="section" id="training-the-network">
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<h3>Training the network<a class="headerlink" href="#training-the-network" title="Permalink to this headline">¶</a></h3>
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<p>Train the network only when the DND has enough entries for querying.</p>
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<p>To train the network, the current states are used as the inputs and the <span class="math notranslate nohighlight">\(N\)</span>-step returns are used as the targets.
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The <span class="math notranslate nohighlight">\(N\)</span>-step return used takes into account <span class="math notranslate nohighlight">\(N\)</span> consecutive steps, and bootstraps the last value from
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the network if necessary:
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<span class="math notranslate nohighlight">\(y_t=\sum_{j=0}^{N-1}\gamma^j r(s_{t+j},a_{t+j} ) +\gamma^N max_a Q(s_{t+N},a)\)</span></p>
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<dl class="class">
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<dt id="rl_coach.agents.nec_agent.NECAlgorithmParameters">
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<em class="property">class </em><code class="descclassname">rl_coach.agents.nec_agent.</code><code class="descname">NECAlgorithmParameters</code><a class="reference internal" href="../../../_modules/rl_coach/agents/nec_agent.html#NECAlgorithmParameters"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#rl_coach.agents.nec_agent.NECAlgorithmParameters" title="Permalink to this definition">¶</a></dt>
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<dd><table class="docutils field-list" frame="void" rules="none">
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<col class="field-name" />
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<col class="field-body" />
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<tbody valign="top">
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
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<li><strong>dnd_size</strong> – (int)
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Defines the number of transitions that will be stored in each one of the DNDs. Note that the total number
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of transitions that will be stored is dnd_size x num_actions.</li>
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<li><strong>l2_norm_added_delta</strong> – (float)
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A small value that will be added when calculating the weight of each of the DND entries. This follows the
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<span class="math notranslate nohighlight">\(\delta\)</span> patameter defined in the paper.</li>
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<li><strong>new_value_shift_coefficient</strong> – (float)
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In the case where a ew embedding that was added to the DND was already present, the value that will be stored
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in the DND is a mix between the existing value and the new value. The mix rate is defined by
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new_value_shift_coefficient.</li>
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<li><strong>number_of_knn</strong> – (int)
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The number of neighbors that will be retrieved for each DND query.</li>
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<li><strong>DND_key_error_threshold</strong> – (float)
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When the DND is queried for a specific embedding, this threshold will be used to determine if the embedding
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exists in the DND, since exact matches of embeddings are very rare.</li>
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<li><strong>propagate_updates_to_DND</strong> – (bool)
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If set to True, when the gradients of the network will be calculated, the gradients will also be
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backpropagated through the keys of the DND. The keys will then be updated as well, as if they were regular
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network weights.</li>
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<li><strong>n_step</strong> – (int)
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The bootstrap length that will be used when calculating the state values to store in the DND.</li>
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<li><strong>bootstrap_total_return_from_old_policy</strong> – (bool)
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If set to True, the bootstrap that will be used to calculate each state-action value, is the network value
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when the state was first seen, and not the latest, most up-to-date network value.</li>
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</ul>
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</td>
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</tr>
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</tbody>
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</table>
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</dd></dl>
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