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Enabling Coach Documentation to be run even when environments are not installed (#326)
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anabwan
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<title>Rainbow — Reinforcement Learning Coach 0.11.0 documentation</title>
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<title>Rainbow — Reinforcement Learning Coach 0.12.1 documentation</title>
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<script type="text/javascript" src="../../../_static/js/modernizr.min.js"></script>
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<script type="text/javascript" id="documentation_options" data-url_root="../../../" src="../../../_static/documentation_options.js"></script>
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<script type="text/javascript" src="../../../_static/jquery.js"></script>
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<script type="text/javascript" src="../../../_static/underscore.js"></script>
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<script type="text/javascript" src="../../../_static/doctools.js"></script>
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<script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
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<link rel="stylesheet" href="../../../_static/css/theme.css" type="text/css" />
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<link rel="stylesheet" href="../../../_static/pygments.css" type="text/css" />
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<link rel="prev" title="Proximal Policy Optimization" href="../policy_optimization/ppo.html" />
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<link href="../../../_static/css/custom.css" rel="stylesheet" type="text/css">
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<script src="../../../_static/js/modernizr.min.js"></script>
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</head>
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<body class="wy-body-for-nav">
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<div class="wy-grid-for-nav">
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<div class="wy-side-nav-search">
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<div class="wy-side-nav-search" >
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@@ -226,19 +229,18 @@
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<h2>Algorithm Description<a class="headerlink" href="#algorithm-description" title="Permalink to this headline">¶</a></h2>
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<p>Rainbow combines 6 recent advancements in reinforcement learning:</p>
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<ul class="simple">
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<li>N-step returns</li>
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<li>Distributional state-action value learning</li>
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<li>Dueling networks</li>
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<li>Noisy Networks</li>
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<li>Double DQN</li>
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<li>Prioritized Experience Replay</li>
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<li><p>N-step returns</p></li>
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<li><p>Distributional state-action value learning</p></li>
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<li><p>Dueling networks</p></li>
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<li><p>Noisy Networks</p></li>
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<li><p>Double DQN</p></li>
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<li><p>Prioritized Experience Replay</p></li>
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</ul>
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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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<ol class="arabic">
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<li><p class="first">Sample a batch of transitions from the replay buffer.</p>
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</li>
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<li><p class="first">The Bellman update is projected to the set of atoms representing the <span class="math notranslate nohighlight">\(Q\)</span> values distribution, such
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<li><p>Sample a batch of transitions from the replay buffer.</p></li>
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<li><p>The Bellman update is projected to the set of atoms representing the <span class="math notranslate nohighlight">\(Q\)</span> values distribution, such
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that the <span class="math notranslate nohighlight">\(i-th\)</span> component of the projected update is calculated as follows:</p>
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<p><span class="math notranslate nohighlight">\((\Phi \hat{T} Z_{\theta}(s_t,a_t))_i=\sum_{j=0}^{N-1}\Big[1-\frac{\lvert[\hat{T}_{z_{j}}]^{V_{MAX}}_{V_{MIN}}-z_i\rvert}{\Delta z}\Big]^1_0 \ p_j(s_{t+1}, \pi(s_{t+1}))\)</span></p>
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<p>where:
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@@ -246,36 +248,29 @@ that the <span class="math notranslate nohighlight">\(i-th\)</span> component of
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* <span class="math notranslate nohighlight">\(\hat{T}_{z_{j}}\)</span> is the Bellman update for atom
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<span class="math notranslate nohighlight">\(z_j\)</span>: <span class="math notranslate nohighlight">\(\hat{T}_{z_{j}} := r_t+\gamma r_{t+1} + ... + \gamma r_{t+n-1} + \gamma^{n-1} z_j\)</span></p>
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</li>
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<li><p class="first">Network is trained with the cross entropy loss between the resulting probability distribution and the target
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probability distribution. Only the target of the actions that were actually taken is updated.</p>
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</li>
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<li><p class="first">Once in every few thousand steps, weights are copied from the online network to the target network.</p>
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</li>
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<li><p class="first">After every training step, the priorities of the batch transitions are updated in the prioritized replay buffer
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using the KL divergence loss that is returned from the network.</p>
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</li>
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<li><p>Network is trained with the cross entropy loss between the resulting probability distribution and the target
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probability distribution. Only the target of the actions that were actually taken is updated.</p></li>
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<li><p>Once in every few thousand steps, weights are copied from the online network to the target network.</p></li>
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<li><p>After every training step, the priorities of the batch transitions are updated in the prioritized replay buffer
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using the KL divergence loss that is returned from the network.</p></li>
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</ol>
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<dl class="class">
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<dt id="rl_coach.agents.rainbow_dqn_agent.RainbowDQNAlgorithmParameters">
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<em class="property">class </em><code class="descclassname">rl_coach.agents.rainbow_dqn_agent.</code><code class="descname">RainbowDQNAlgorithmParameters</code><a class="reference internal" href="../../../_modules/rl_coach/agents/rainbow_dqn_agent.html#RainbowDQNAlgorithmParameters"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#rl_coach.agents.rainbow_dqn_agent.RainbowDQNAlgorithmParameters" 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>n_step</strong> – (int)
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<dd><dl class="field-list simple">
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<dt class="field-odd">Parameters</dt>
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<dd class="field-odd"><ul class="simple">
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<li><p><strong>n_step</strong> – (int)
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The number of steps to bootstrap the network over. The first N-1 steps actual rewards will be accumulated
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using an exponentially growing discount factor, and the Nth step will be bootstrapped from the network
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prediction.</li>
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<li><strong>store_transitions_only_when_episodes_are_terminated</strong> – (bool)
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prediction.</p></li>
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<li><p><strong>store_transitions_only_when_episodes_are_terminated</strong> – (bool)
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If set to True, the transitions will be stored in an Episode object until the episode ends, and just then
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written to the memory. This is useful since we want to calculate the N-step discounted rewards before saving the
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transitions into the memory, and to do so we need the entire episode first.</li>
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transitions into the memory, and to do so we need the entire episode first.</p></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>
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</dl>
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</dd></dl>
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</div>
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@@ -293,7 +288,7 @@ transitions into the memory, and to do so we need the entire episode first.</li>
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<a href="qr_dqn.html" class="btn btn-neutral float-right" title="Quantile Regression DQN" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right"></span></a>
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<a href="../policy_optimization/ppo.html" class="btn btn-neutral" title="Proximal Policy Optimization" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left"></span> Previous</a>
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<a href="../policy_optimization/ppo.html" class="btn btn-neutral float-left" title="Proximal Policy Optimization" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left"></span> Previous</a>
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</div>
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@@ -302,7 +297,7 @@ transitions into the memory, and to do so we need the entire episode first.</li>
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<div role="contentinfo">
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<p>
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© Copyright 2018, Intel AI Lab
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© Copyright 2018-2019, Intel AI Lab
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</p>
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</div>
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@@ -319,27 +314,16 @@ transitions into the memory, and to do so we need the entire episode first.</li>
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<script type="text/javascript" id="documentation_options" data-url_root="../../../" src="../../../_static/documentation_options.js"></script>
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<script type="text/javascript" src="../../../_static/jquery.js"></script>
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<script type="text/javascript" src="../../../_static/underscore.js"></script>
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<script type="text/javascript" src="../../../_static/doctools.js"></script>
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<script type="text/javascript" src="../../../_static/language_data.js"></script>
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<script async="async" type="text/javascript" src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-AMS-MML_HTMLorMML"></script>
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<script type="text/javascript" src="../../../_static/js/theme.js"></script>
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<script type="text/javascript">
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jQuery(function () {
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SphinxRtdTheme.Navigation.enable(true);
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});
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</script>
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</script>
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</body>
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</html>
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