mirror of
https://github.com/gryf/coach.git
synced 2025-12-18 03:30:19 +01:00
Enabling Coach Documentation to be run even when environments are not installed (#326)
This commit is contained in:
anabwan
@@ -8,7 +8,7 @@
|
||||
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0">
|
||||
|
||||
<title>N-Step Q Learning — Reinforcement Learning Coach 0.11.0 documentation</title>
|
||||
<title>N-Step Q Learning — Reinforcement Learning Coach 0.12.1 documentation</title>
|
||||
|
||||
|
||||
|
||||
@@ -17,13 +17,21 @@
|
||||
|
||||
|
||||
|
||||
<script type="text/javascript" src="../../../_static/js/modernizr.min.js"></script>
|
||||
|
||||
|
||||
<script type="text/javascript" id="documentation_options" data-url_root="../../../" src="../../../_static/documentation_options.js"></script>
|
||||
<script type="text/javascript" src="../../../_static/jquery.js"></script>
|
||||
<script type="text/javascript" src="../../../_static/underscore.js"></script>
|
||||
<script type="text/javascript" src="../../../_static/doctools.js"></script>
|
||||
<script type="text/javascript" src="../../../_static/language_data.js"></script>
|
||||
<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>
|
||||
|
||||
<script type="text/javascript" src="../../../_static/js/theme.js"></script>
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
<link rel="stylesheet" href="../../../_static/css/theme.css" type="text/css" />
|
||||
<link rel="stylesheet" href="../../../_static/pygments.css" type="text/css" />
|
||||
<link rel="stylesheet" href="../../../_static/css/custom.css" type="text/css" />
|
||||
@@ -33,21 +41,16 @@
|
||||
<link rel="prev" title="Mixed Monte Carlo" href="mmc.html" />
|
||||
<link href="../../../_static/css/custom.css" rel="stylesheet" type="text/css">
|
||||
|
||||
|
||||
|
||||
<script src="../../../_static/js/modernizr.min.js"></script>
|
||||
|
||||
</head>
|
||||
|
||||
<body class="wy-body-for-nav">
|
||||
|
||||
|
||||
<div class="wy-grid-for-nav">
|
||||
|
||||
|
||||
<nav data-toggle="wy-nav-shift" class="wy-nav-side">
|
||||
<div class="wy-side-scroll">
|
||||
<div class="wy-side-nav-search">
|
||||
<div class="wy-side-nav-search" >
|
||||
|
||||
|
||||
|
||||
@@ -228,43 +231,39 @@
|
||||
<h3>Training the network<a class="headerlink" href="#training-the-network" title="Permalink to this headline">¶</a></h3>
|
||||
<p>The <span class="math notranslate nohighlight">\(N\)</span>-step Q learning algorithm works in similar manner to DQN except for the following changes:</p>
|
||||
<ol class="arabic simple">
|
||||
<li>No replay buffer is used. Instead of sampling random batches of transitions, the network is trained every
|
||||
<span class="math notranslate nohighlight">\(N\)</span> steps using the latest <span class="math notranslate nohighlight">\(N\)</span> steps played by the agent.</li>
|
||||
<li>In order to stabilize the learning, multiple workers work together to update the network.
|
||||
This creates the same effect as uncorrelating the samples used for training.</li>
|
||||
<li>Instead of using single-step Q targets for the network, the rewards from $N$ consequent steps are accumulated
|
||||
<li><p>No replay buffer is used. Instead of sampling random batches of transitions, the network is trained every
|
||||
<span class="math notranslate nohighlight">\(N\)</span> steps using the latest <span class="math notranslate nohighlight">\(N\)</span> steps played by the agent.</p></li>
|
||||
<li><p>In order to stabilize the learning, multiple workers work together to update the network.
|
||||
This creates the same effect as uncorrelating the samples used for training.</p></li>
|
||||
<li><p>Instead of using single-step Q targets for the network, the rewards from $N$ consequent steps are accumulated
|
||||
to form the <span class="math notranslate nohighlight">\(N\)</span>-step Q targets, according to the following equation:
|
||||
<span class="math notranslate nohighlight">\(R(s_t, a_t) = \sum_{i=t}^{i=t + k - 1} \gamma^{i-t}r_i +\gamma^{k} V(s_{t+k})\)</span>
|
||||
where <span class="math notranslate nohighlight">\(k\)</span> is <span class="math notranslate nohighlight">\(T_{max} - State\_Index\)</span> for each state in the batch</li>
|
||||
where <span class="math notranslate nohighlight">\(k\)</span> is <span class="math notranslate nohighlight">\(T_{max} - State\_Index\)</span> for each state in the batch</p></li>
|
||||
</ol>
|
||||
<dl class="class">
|
||||
<dt id="rl_coach.agents.n_step_q_agent.NStepQAlgorithmParameters">
|
||||
<em class="property">class </em><code class="descclassname">rl_coach.agents.n_step_q_agent.</code><code class="descname">NStepQAlgorithmParameters</code><a class="reference internal" href="../../../_modules/rl_coach/agents/n_step_q_agent.html#NStepQAlgorithmParameters"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#rl_coach.agents.n_step_q_agent.NStepQAlgorithmParameters" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><table class="docutils field-list" frame="void" rules="none">
|
||||
<col class="field-name" />
|
||||
<col class="field-body" />
|
||||
<tbody valign="top">
|
||||
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
|
||||
<li><strong>num_steps_between_copying_online_weights_to_target</strong> – (StepMethod)
|
||||
The number of steps between copying the online network weights to the target network weights.</li>
|
||||
<li><strong>apply_gradients_every_x_episodes</strong> – (int)
|
||||
<dd><dl class="field-list simple">
|
||||
<dt class="field-odd">Parameters</dt>
|
||||
<dd class="field-odd"><ul class="simple">
|
||||
<li><p><strong>num_steps_between_copying_online_weights_to_target</strong> – (StepMethod)
|
||||
The number of steps between copying the online network weights to the target network weights.</p></li>
|
||||
<li><p><strong>apply_gradients_every_x_episodes</strong> – (int)
|
||||
The number of episodes between applying the accumulated gradients to the network. After every
|
||||
num_steps_between_gradient_updates steps, the agent will calculate the gradients for the collected data,
|
||||
it will then accumulate it in internal accumulators, and will only apply them to the network once in every
|
||||
apply_gradients_every_x_episodes episodes.</li>
|
||||
<li><strong>num_steps_between_gradient_updates</strong> – (int)
|
||||
apply_gradients_every_x_episodes episodes.</p></li>
|
||||
<li><p><strong>num_steps_between_gradient_updates</strong> – (int)
|
||||
The number of steps between calculating gradients for the collected data. In the A3C paper, this parameter is
|
||||
called t_max. Since this algorithm is on-policy, only the steps collected between each two gradient calculations
|
||||
are used in the batch.</li>
|
||||
<li><strong>targets_horizon</strong> – (str)
|
||||
are used in the batch.</p></li>
|
||||
<li><p><strong>targets_horizon</strong> – (str)
|
||||
Should be either ‘N-Step’ or ‘1-Step’, and defines the length for which to bootstrap the network values over.
|
||||
Essentially, 1-Step follows the regular 1 step bootstrapping Q learning update. For more information,
|
||||
please refer to the original paper (<a class="reference external" href="https://arxiv.org/abs/1602.01783">https://arxiv.org/abs/1602.01783</a>)</li>
|
||||
please refer to the original paper (<a class="reference external" href="https://arxiv.org/abs/1602.01783">https://arxiv.org/abs/1602.01783</a>)</p></li>
|
||||
</ul>
|
||||
</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
</dd>
|
||||
</dl>
|
||||
</dd></dl>
|
||||
|
||||
</div>
|
||||
@@ -282,7 +281,7 @@ please refer to the original paper (<a class="reference external" href="https://
|
||||
<a href="naf.html" class="btn btn-neutral float-right" title="Normalized Advantage Functions" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right"></span></a>
|
||||
|
||||
|
||||
<a href="mmc.html" class="btn btn-neutral" title="Mixed Monte Carlo" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left"></span> Previous</a>
|
||||
<a href="mmc.html" class="btn btn-neutral float-left" title="Mixed Monte Carlo" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left"></span> Previous</a>
|
||||
|
||||
</div>
|
||||
|
||||
@@ -291,7 +290,7 @@ please refer to the original paper (<a class="reference external" href="https://
|
||||
|
||||
<div role="contentinfo">
|
||||
<p>
|
||||
© Copyright 2018, Intel AI Lab
|
||||
© Copyright 2018-2019, Intel AI Lab
|
||||
|
||||
</p>
|
||||
</div>
|
||||
@@ -308,27 +307,16 @@ please refer to the original paper (<a class="reference external" href="https://
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
<script type="text/javascript" id="documentation_options" data-url_root="../../../" src="../../../_static/documentation_options.js"></script>
|
||||
<script type="text/javascript" src="../../../_static/jquery.js"></script>
|
||||
<script type="text/javascript" src="../../../_static/underscore.js"></script>
|
||||
<script type="text/javascript" src="../../../_static/doctools.js"></script>
|
||||
<script type="text/javascript" src="../../../_static/language_data.js"></script>
|
||||
<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>
|
||||
|
||||
|
||||
|
||||
|
||||
<script type="text/javascript" src="../../../_static/js/theme.js"></script>
|
||||
|
||||
<script type="text/javascript">
|
||||
jQuery(function () {
|
||||
SphinxRtdTheme.Navigation.enable(true);
|
||||
});
|
||||
</script>
|
||||
</script>
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
</body>
|
||||
</html>
|
||||
Reference in New Issue
Block a user