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mirror of https://github.com/gryf/coach.git synced 2025-12-18 11:40:18 +01:00

Enabling Coach Documentation to be run even when environments are not installed (#326)

This commit is contained in:
anabwan
2019-05-27 10:46:07 +03:00
committed by Gal Leibovich
parent 2b7d536da4
commit 342b7184bc
157 changed files with 5167 additions and 7477 deletions

View File

@@ -8,7 +8,7 @@
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Behavioral Cloning &mdash; Reinforcement Learning Coach 0.11.0 documentation</title>
<title>Behavioral Cloning &mdash; 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>
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@@ -33,21 +41,16 @@
<link rel="prev" title="ACER" href="../policy_optimization/acer.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">
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@@ -230,9 +233,9 @@ These demonstrations are given as state, action tuples, and with no reward.
The training goal is to reduce the difference between the actions predicted by the network and the actions taken by
the expert for each state.</p>
<ol class="arabic simple">
<li>Sample a batch of transitions from the replay buffer.</li>
<li>Use the current states as input to the network, and the expert actions as the targets of the network.</li>
<li>For the network head, we use the policy head, which uses the cross entropy loss function.</li>
<li><p>Sample a batch of transitions from the replay buffer.</p></li>
<li><p>Use the current states as input to the network, and the expert actions as the targets of the network.</p></li>
<li><p>For the network head, we use the policy head, which uses the cross entropy loss function.</p></li>
</ol>
<dl class="class">
<dt id="rl_coach.agents.bc_agent.BCAlgorithmParameters">
@@ -254,7 +257,7 @@ the expert for each state.</p>
<a href="../value_optimization/bs_dqn.html" class="btn btn-neutral float-right" title="Bootstrapped DQN" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right"></span></a>
<a href="../policy_optimization/acer.html" class="btn btn-neutral" title="ACER" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left"></span> Previous</a>
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</div>
@@ -263,7 +266,7 @@ the expert for each state.</p>
<div role="contentinfo">
<p>
&copy; Copyright 2018, Intel AI Lab
&copy; Copyright 2018-2019, Intel AI Lab
</p>
</div>
@@ -280,27 +283,16 @@ the expert for each state.</p>
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View File

@@ -8,7 +8,7 @@
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Conditional Imitation Learning &mdash; Reinforcement Learning Coach 0.11.0 documentation</title>
<title>Conditional Imitation Learning &mdash; 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>
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@@ -33,21 +41,16 @@
<link rel="prev" title="Categorical DQN" href="../value_optimization/categorical_dqn.html" />
<link href="../../../_static/css/custom.css" rel="stylesheet" type="text/css">
<script src="../../../_static/js/modernizr.min.js"></script>
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@@ -233,25 +236,22 @@ the expert for each state.
In conditional imitation learning, each transition is assigned a class, which determines the goal that was pursuit
in that transitions. For example, 3 possible classes can be: turn right, turn left and follow lane.</p>
<ol class="arabic simple">
<li>Sample a batch of transitions from the replay buffer, where the batch is balanced, meaning that an equal number
of transitions will be sampled from each class index.</li>
<li>Use the current states as input to the network, and assign the expert actions as the targets of the network heads
<li><p>Sample a batch of transitions from the replay buffer, where the batch is balanced, meaning that an equal number
of transitions will be sampled from each class index.</p></li>
<li><p>Use the current states as input to the network, and assign the expert actions as the targets of the network heads
corresponding to the state classes. For the other heads, set the targets to match the currently predicted values,
so that the loss for the other heads will be zeroed out.</li>
<li>We use a regression head, that minimizes the MSE loss between the network predicted values and the target values.</li>
so that the loss for the other heads will be zeroed out.</p></li>
<li><p>We use a regression head, that minimizes the MSE loss between the network predicted values and the target values.</p></li>
</ol>
<dl class="class">
<dt id="rl_coach.agents.cil_agent.CILAlgorithmParameters">
<em class="property">class </em><code class="descclassname">rl_coach.agents.cil_agent.</code><code class="descname">CILAlgorithmParameters</code><a class="reference internal" href="../../../_modules/rl_coach/agents/cil_agent.html#CILAlgorithmParameters"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#rl_coach.agents.cil_agent.CILAlgorithmParameters" 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"><strong>state_key_with_the_class_index</strong> (str)
The key of the state dictionary which corresponds to the value that will be used to control the class index.</td>
</tr>
</tbody>
</table>
<dd><dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>state_key_with_the_class_index</strong> (str)
The key of the state dictionary which corresponds to the value that will be used to control the class index.</p>
</dd>
</dl>
</dd></dl>
</div>
@@ -269,7 +269,7 @@ The key of the state dictionary which corresponds to the value that will be used
<a href="../policy_optimization/cppo.html" class="btn btn-neutral float-right" title="Clipped Proximal Policy Optimization" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right"></span></a>
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</div>
@@ -278,7 +278,7 @@ The key of the state dictionary which corresponds to the value that will be used
<div role="contentinfo">
<p>
&copy; Copyright 2018, Intel AI Lab
&copy; Copyright 2018-2019, Intel AI Lab
</p>
</div>
@@ -295,27 +295,16 @@ The key of the state dictionary which corresponds to the value that will be used
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