mirror of
https://github.com/gryf/coach.git
synced 2026-04-27 11:15:40 +02:00
Enabling Coach Documentation to be run even when environments are not installed (#326)
This commit is contained in:
anabwan
+16
-24
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<title>Overview: module code — Reinforcement Learning Coach 0.11.0 documentation</title>
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<title>Overview: module code — Reinforcement Learning Coach 0.12.1 documentation</title>
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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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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<title>rl_coach.agents.acer_agent — Reinforcement Learning Coach 0.11.0 documentation</title>
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<title>rl_coach.agents.acer_agent — Reinforcement Learning Coach 0.12.1 documentation</title>
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<link rel="search" title="Search" href="../../../search.html" />
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<link href="../../../_static/css/custom.css" rel="stylesheet" type="text/css">
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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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<script type="text/javascript" id="documentation_options" data-url_root="../../../" src="../../../_static/documentation_options.js"></script>
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<title>rl_coach.agents.actor_critic_agent — Reinforcement Learning Coach 0.11.0 documentation</title>
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<title>rl_coach.agents.actor_critic_agent — 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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<link rel="search" title="Search" href="../../../search.html" />
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<link href="../../../_static/css/custom.css" rel="stylesheet" type="text/css">
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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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<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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});
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<title>rl_coach.agents.agent — Reinforcement Learning Coach 0.11.0 documentation</title>
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<title>rl_coach.agents.agent — Reinforcement Learning Coach 0.12.1 documentation</title>
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<script type="text/javascript" id="documentation_options" data-url_root="../../../" src="../../../_static/documentation_options.js"></script>
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<link rel="search" title="Search" href="../../../search.html" />
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<link href="../../../_static/css/custom.css" rel="stylesheet" type="text/css">
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<span class="bp">self</span><span class="o">.</span><span class="n">agent_logger</span><span class="o">.</span><span class="n">create_signal_value</span><span class="p">(</span><span class="s1">'Inverse Propensity Score'</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span> <span class="n">overwrite</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">agent_logger</span><span class="o">.</span><span class="n">create_signal_value</span><span class="p">(</span><span class="s1">'Direct Method Reward'</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span> <span class="n">overwrite</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">agent_logger</span><span class="o">.</span><span class="n">create_signal_value</span><span class="p">(</span><span class="s1">'Doubly Robust'</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span> <span class="n">overwrite</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">agent_logger</span><span class="o">.</span><span class="n">create_signal_value</span><span class="p">(</span><span class="s1">'Weighted Importance Sampling'</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span> <span class="n">overwrite</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">agent_logger</span><span class="o">.</span><span class="n">create_signal_value</span><span class="p">(</span><span class="s1">'Sequential Doubly Robust'</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span> <span class="n">overwrite</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
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<span class="k">for</span> <span class="n">signal</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">episode_signals</span><span class="p">:</span>
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<span class="c1"># we either go sequentially through the entire replay buffer in the batch RL mode,</span>
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<span class="c1"># or sample randomly for the basic RL case.</span>
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<span class="n">training_schedule</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">call_memory</span><span class="p">(</span><span class="s1">'get_shuffled_data_generator'</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">)</span> <span class="k">if</span> \
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<span class="n">training_schedule</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">call_memory</span><span class="p">(</span><span class="s1">'get_shuffled_training_data_generator'</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">)</span> <span class="k">if</span> \
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<span class="bp">self</span><span class="o">.</span><span class="n">ap</span><span class="o">.</span><span class="n">is_batch_rl_training</span> <span class="k">else</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">call_memory</span><span class="p">(</span><span class="s1">'sample'</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">)</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span>
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<span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">ap</span><span class="o">.</span><span class="n">algorithm</span><span class="o">.</span><span class="n">num_consecutive_training_steps</span><span class="p">)]</span>
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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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<title>rl_coach.agents.agent_interface — Reinforcement Learning Coach 0.11.0 documentation</title>
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<title>rl_coach.agents.agent_interface — Reinforcement Learning Coach 0.12.1 documentation</title>
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|
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<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>
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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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@@ -8,7 +8,7 @@
|
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||||
<meta name="viewport" content="width=device-width, initial-scale=1.0">
|
||||
|
||||
<title>rl_coach.agents.soft_actor_critic_agent — Reinforcement Learning Coach 0.11.0 documentation</title>
|
||||
<title>rl_coach.agents.soft_actor_critic_agent — Reinforcement Learning Coach 0.12.1 documentation</title>
|
||||
|
||||
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||||
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@@ -17,9 +17,17 @@
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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>
|
||||
<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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@@ -31,10 +39,6 @@
|
||||
<link rel="search" title="Search" href="../../../search.html" />
|
||||
<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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@@ -42,10 +46,9 @@
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<div class="wy-grid-for-nav">
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<nav data-toggle="wy-nav-shift" class="wy-nav-side">
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<div class="wy-side-scroll">
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<div class="wy-side-nav-search">
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<div class="wy-side-nav-search" >
|
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@@ -511,7 +514,7 @@
|
||||
|
||||
<div role="contentinfo">
|
||||
<p>
|
||||
© Copyright 2018, Intel AI Lab
|
||||
© Copyright 2018-2019, Intel AI Lab
|
||||
|
||||
</p>
|
||||
</div>
|
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@@ -528,27 +531,16 @@
|
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<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>
|
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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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</body>
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</html>
|
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@@ -8,7 +8,7 @@
|
||||
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0">
|
||||
|
||||
<title>rl_coach.agents.value_optimization_agent — Reinforcement Learning Coach 0.11.0 documentation</title>
|
||||
<title>rl_coach.agents.value_optimization_agent — Reinforcement Learning Coach 0.12.1 documentation</title>
|
||||
|
||||
|
||||
|
||||
@@ -17,9 +17,17 @@
|
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|
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|
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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>
|
||||
<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>
|
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<script type="text/javascript" src="../../../_static/js/theme.js"></script>
|
||||
|
||||
|
||||
|
||||
@@ -31,10 +39,6 @@
|
||||
<link rel="search" title="Search" href="../../../search.html" />
|
||||
<link href="../../../_static/css/custom.css" rel="stylesheet" type="text/css">
|
||||
|
||||
|
||||
|
||||
<script src="../../../_static/js/modernizr.min.js"></script>
|
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|
||||
</head>
|
||||
|
||||
<body class="wy-body-for-nav">
|
||||
@@ -42,10 +46,9 @@
|
||||
|
||||
<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" >
|
||||
|
||||
|
||||
|
||||
@@ -200,6 +203,7 @@
|
||||
|
||||
<span class="kn">from</span> <span class="nn">rl_coach.agents.agent</span> <span class="k">import</span> <span class="n">Agent</span>
|
||||
<span class="kn">from</span> <span class="nn">rl_coach.core_types</span> <span class="k">import</span> <span class="n">ActionInfo</span><span class="p">,</span> <span class="n">StateType</span><span class="p">,</span> <span class="n">Batch</span>
|
||||
<span class="kn">from</span> <span class="nn">rl_coach.filters.filter</span> <span class="k">import</span> <span class="n">NoInputFilter</span>
|
||||
<span class="kn">from</span> <span class="nn">rl_coach.logger</span> <span class="k">import</span> <span class="n">screen</span>
|
||||
<span class="kn">from</span> <span class="nn">rl_coach.memories.non_episodic.prioritized_experience_replay</span> <span class="k">import</span> <span class="n">PrioritizedExperienceReplay</span>
|
||||
<span class="kn">from</span> <span class="nn">rl_coach.spaces</span> <span class="k">import</span> <span class="n">DiscreteActionSpace</span>
|
||||
@@ -288,18 +292,18 @@
|
||||
<span class="sd"> :return: None</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">ope_manager</span>
|
||||
<span class="n">dataset_as_episodes</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">call_memory</span><span class="p">(</span><span class="s1">'get_all_complete_episodes_from_to'</span><span class="p">,</span>
|
||||
<span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">call_memory</span><span class="p">(</span><span class="s1">'get_last_training_set_episode_id'</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">call_memory</span><span class="p">(</span><span class="s1">'num_complete_episodes'</span><span class="p">)))</span>
|
||||
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">dataset_as_episodes</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
|
||||
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'train_to_eval_ratio is too high causing the evaluation set to be empty. '</span>
|
||||
<span class="s1">'Consider decreasing its value.'</span><span class="p">)</span>
|
||||
|
||||
<span class="n">ips</span><span class="p">,</span> <span class="n">dm</span><span class="p">,</span> <span class="n">dr</span><span class="p">,</span> <span class="n">seq_dr</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">ope_manager</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span>
|
||||
<span class="n">dataset_as_episodes</span><span class="o">=</span><span class="n">dataset_as_episodes</span><span class="p">,</span>
|
||||
<span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">pre_network_filter</span><span class="p">,</span> <span class="n">NoInputFilter</span><span class="p">)</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">pre_network_filter</span><span class="o">.</span><span class="n">reward_filters</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
|
||||
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"Defining a pre-network reward filter when OPEs are calculated will result in a mismatch "</span>
|
||||
<span class="s2">"between q values (which are scaled), and actual rewards, which are not. It is advisable "</span>
|
||||
<span class="s2">"to use an input_filter, if possible, instead, which will filter the transitions directly "</span>
|
||||
<span class="s2">"in the replay buffer, affecting both the q_values and the rewards themselves. "</span><span class="p">)</span>
|
||||
|
||||
<span class="n">ips</span><span class="p">,</span> <span class="n">dm</span><span class="p">,</span> <span class="n">dr</span><span class="p">,</span> <span class="n">seq_dr</span><span class="p">,</span> <span class="n">wis</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">ope_manager</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span>
|
||||
<span class="n">evaluation_dataset_as_episodes</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">memory</span><span class="o">.</span><span class="n">evaluation_dataset_as_episodes</span><span class="p">,</span>
|
||||
<span class="n">evaluation_dataset_as_transitions</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">memory</span><span class="o">.</span><span class="n">evaluation_dataset_as_transitions</span><span class="p">,</span>
|
||||
<span class="n">batch_size</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">ap</span><span class="o">.</span><span class="n">network_wrappers</span><span class="p">[</span><span class="s1">'main'</span><span class="p">]</span><span class="o">.</span><span class="n">batch_size</span><span class="p">,</span>
|
||||
<span class="n">discount_factor</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">ap</span><span class="o">.</span><span class="n">algorithm</span><span class="o">.</span><span class="n">discount</span><span class="p">,</span>
|
||||
<span class="n">reward_model</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">networks</span><span class="p">[</span><span class="s1">'reward_model'</span><span class="p">]</span><span class="o">.</span><span class="n">online_network</span><span class="p">,</span>
|
||||
<span class="n">q_network</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">networks</span><span class="p">[</span><span class="s1">'main'</span><span class="p">]</span><span class="o">.</span><span class="n">online_network</span><span class="p">,</span>
|
||||
<span class="n">network_keys</span><span class="o">=</span><span class="nb">list</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">ap</span><span class="o">.</span><span class="n">network_wrappers</span><span class="p">[</span><span class="s1">'main'</span><span class="p">]</span><span class="o">.</span><span class="n">input_embedders_parameters</span><span class="o">.</span><span class="n">keys</span><span class="p">()))</span>
|
||||
|
||||
@@ -309,6 +313,7 @@
|
||||
<span class="n">log</span><span class="p">[</span><span class="s1">'IPS'</span><span class="p">]</span> <span class="o">=</span> <span class="n">ips</span>
|
||||
<span class="n">log</span><span class="p">[</span><span class="s1">'DM'</span><span class="p">]</span> <span class="o">=</span> <span class="n">dm</span>
|
||||
<span class="n">log</span><span class="p">[</span><span class="s1">'DR'</span><span class="p">]</span> <span class="o">=</span> <span class="n">dr</span>
|
||||
<span class="n">log</span><span class="p">[</span><span class="s1">'WIS'</span><span class="p">]</span> <span class="o">=</span> <span class="n">wis</span>
|
||||
<span class="n">log</span><span class="p">[</span><span class="s1">'Sequential-DR'</span><span class="p">]</span> <span class="o">=</span> <span class="n">seq_dr</span>
|
||||
<span class="n">screen</span><span class="o">.</span><span class="n">log_dict</span><span class="p">(</span><span class="n">log</span><span class="p">,</span> <span class="n">prefix</span><span class="o">=</span><span class="s1">'Off-Policy Evaluation'</span><span class="p">)</span>
|
||||
|
||||
@@ -318,6 +323,7 @@
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">agent_logger</span><span class="o">.</span><span class="n">create_signal_value</span><span class="p">(</span><span class="s1">'Direct Method Reward'</span><span class="p">,</span> <span class="n">dm</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">agent_logger</span><span class="o">.</span><span class="n">create_signal_value</span><span class="p">(</span><span class="s1">'Doubly Robust'</span><span class="p">,</span> <span class="n">dr</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">agent_logger</span><span class="o">.</span><span class="n">create_signal_value</span><span class="p">(</span><span class="s1">'Sequential Doubly Robust'</span><span class="p">,</span> <span class="n">seq_dr</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">agent_logger</span><span class="o">.</span><span class="n">create_signal_value</span><span class="p">(</span><span class="s1">'Weighted Importance Sampling'</span><span class="p">,</span> <span class="n">wis</span><span class="p">)</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">get_reward_model_loss</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">batch</span><span class="p">:</span> <span class="n">Batch</span><span class="p">):</span>
|
||||
<span class="n">network_keys</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">ap</span><span class="o">.</span><span class="n">network_wrappers</span><span class="p">[</span><span class="s1">'reward_model'</span><span class="p">]</span><span class="o">.</span><span class="n">input_embedders_parameters</span><span class="o">.</span><span class="n">keys</span><span class="p">()</span>
|
||||
@@ -341,7 +347,7 @@
|
||||
<span class="k">for</span> <span class="n">epoch</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">epochs</span><span class="p">):</span>
|
||||
<span class="n">loss</span> <span class="o">=</span> <span class="mi">0</span>
|
||||
<span class="n">total_transitions_processed</span> <span class="o">=</span> <span class="mi">0</span>
|
||||
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">batch</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">call_memory</span><span class="p">(</span><span class="s1">'get_shuffled_data_generator'</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">)):</span>
|
||||
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">batch</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">call_memory</span><span class="p">(</span><span class="s1">'get_shuffled_training_data_generator'</span><span class="p">,</span> <span class="n">batch_size</span><span class="p">)):</span>
|
||||
<span class="n">batch</span> <span class="o">=</span> <span class="n">Batch</span><span class="p">(</span><span class="n">batch</span><span class="p">)</span>
|
||||
<span class="n">loss</span> <span class="o">+=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_reward_model_loss</span><span class="p">(</span><span class="n">batch</span><span class="p">)</span>
|
||||
<span class="n">total_transitions_processed</span> <span class="o">+=</span> <span class="n">batch</span><span class="o">.</span><span class="n">size</span>
|
||||
@@ -363,7 +369,7 @@
|
||||
|
||||
<div role="contentinfo">
|
||||
<p>
|
||||
© Copyright 2018, Intel AI Lab
|
||||
© Copyright 2018-2019, Intel AI Lab
|
||||
|
||||
</p>
|
||||
</div>
|
||||
@@ -380,27 +386,16 @@
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
<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>
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
</body>
|
||||
</html>
|
||||
@@ -8,7 +8,7 @@
|
||||
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0">
|
||||
|
||||
<title>rl_coach.architectures.architecture — Reinforcement Learning Coach 0.11.0 documentation</title>
|
||||
<title>rl_coach.architectures.architecture — Reinforcement Learning Coach 0.12.1 documentation</title>
|
||||
|
||||
|
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|
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<div class="wy-side-nav-search" >
|
||||
|
||||
|
||||
|
||||
@@ -387,7 +390,7 @@
|
||||
|
||||
<div role="contentinfo">
|
||||
<p>
|
||||
© Copyright 2018, Intel AI Lab
|
||||
© Copyright 2018-2019, Intel AI Lab
|
||||
|
||||
</p>
|
||||
</div>
|
||||
@@ -404,27 +407,16 @@
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
<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>
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
</body>
|
||||
</html>
|
||||
@@ -8,7 +8,7 @@
|
||||
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0">
|
||||
|
||||
<title>rl_coach.environments.carla_environment — Reinforcement Learning Coach 0.11.0 documentation</title>
|
||||
<title>rl_coach.environments.carla_environment — Reinforcement Learning Coach 0.12.1 documentation</title>
|
||||
|
||||
|
||||
|
||||
@@ -17,9 +17,17 @@
|
||||
|
||||
|
||||
|
||||
<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>
|
||||
|
||||
|
||||
|
||||
@@ -31,10 +39,6 @@
|
||||
<link rel="search" title="Search" href="../../../search.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">
|
||||
@@ -42,10 +46,9 @@
|
||||
|
||||
<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" >
|
||||
|
||||
|
||||
|
||||
@@ -197,7 +200,6 @@
|
||||
<span class="kn">import</span> <span class="nn">random</span>
|
||||
<span class="kn">import</span> <span class="nn">sys</span>
|
||||
<span class="kn">from</span> <span class="nn">os</span> <span class="k">import</span> <span class="n">path</span><span class="p">,</span> <span class="n">environ</span>
|
||||
|
||||
<span class="kn">from</span> <span class="nn">rl_coach.logger</span> <span class="k">import</span> <span class="n">screen</span>
|
||||
<span class="kn">from</span> <span class="nn">rl_coach.filters.action.partial_discrete_action_space_map</span> <span class="k">import</span> <span class="n">PartialDiscreteActionSpaceMap</span>
|
||||
<span class="kn">from</span> <span class="nn">rl_coach.filters.observation.observation_rgb_to_y_filter</span> <span class="k">import</span> <span class="n">ObservationRGBToYFilter</span>
|
||||
@@ -207,7 +209,8 @@
|
||||
<span class="k">if</span> <span class="s1">'CARLA_ROOT'</span> <span class="ow">in</span> <span class="n">environ</span><span class="p">:</span>
|
||||
<span class="n">sys</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">environ</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'CARLA_ROOT'</span><span class="p">),</span> <span class="s1">'PythonClient'</span><span class="p">))</span>
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">screen</span><span class="o">.</span><span class="n">error</span><span class="p">(</span><span class="s2">"CARLA_ROOT was not defined. Please set it to point to the CARLA root directory and try again."</span><span class="p">)</span>
|
||||
<span class="n">screen</span><span class="o">.</span><span class="n">error</span><span class="p">(</span><span class="s2">"CARLA_ROOT was not defined. Please set it to point to the CARLA root directory and try again."</span><span class="p">,</span> <span class="n">crash</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
|
||||
|
||||
<span class="kn">from</span> <span class="nn">carla.client</span> <span class="k">import</span> <span class="n">CarlaClient</span>
|
||||
<span class="kn">from</span> <span class="nn">carla.settings</span> <span class="k">import</span> <span class="n">CarlaSettings</span>
|
||||
<span class="kn">from</span> <span class="nn">carla.tcp</span> <span class="k">import</span> <span class="n">TCPConnectionError</span>
|
||||
@@ -219,21 +222,20 @@
|
||||
<span class="kn">from</span> <span class="nn">rl_coach.logger</span> <span class="k">import</span> <span class="n">failed_imports</span>
|
||||
<span class="n">failed_imports</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s2">"CARLA"</span><span class="p">)</span>
|
||||
|
||||
<span class="kn">import</span> <span class="nn">logging</span>
|
||||
<span class="kn">import</span> <span class="nn">subprocess</span>
|
||||
<span class="kn">from</span> <span class="nn">rl_coach.environments.environment</span> <span class="k">import</span> <span class="n">Environment</span><span class="p">,</span> <span class="n">EnvironmentParameters</span><span class="p">,</span> <span class="n">LevelSelection</span>
|
||||
<span class="kn">from</span> <span class="nn">rl_coach.spaces</span> <span class="k">import</span> <span class="n">BoxActionSpace</span><span class="p">,</span> <span class="n">ImageObservationSpace</span><span class="p">,</span> <span class="n">StateSpace</span><span class="p">,</span> \
|
||||
<span class="n">VectorObservationSpace</span>
|
||||
<span class="kn">from</span> <span class="nn">rl_coach.utils</span> <span class="k">import</span> <span class="n">get_open_port</span><span class="p">,</span> <span class="n">force_list</span>
|
||||
<span class="kn">from</span> <span class="nn">enum</span> <span class="k">import</span> <span class="n">Enum</span>
|
||||
<span class="kn">import</span> <span class="nn">os</span>
|
||||
<span class="kn">import</span> <span class="nn">signal</span>
|
||||
<span class="kn">import</span> <span class="nn">logging</span>
|
||||
<span class="kn">import</span> <span class="nn">subprocess</span>
|
||||
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
|
||||
<span class="kn">from</span> <span class="nn">rl_coach.environments.environment</span> <span class="k">import</span> <span class="n">Environment</span><span class="p">,</span> <span class="n">EnvironmentParameters</span><span class="p">,</span> <span class="n">LevelSelection</span>
|
||||
<span class="kn">from</span> <span class="nn">rl_coach.spaces</span> <span class="k">import</span> <span class="n">BoxActionSpace</span><span class="p">,</span> <span class="n">ImageObservationSpace</span><span class="p">,</span> <span class="n">StateSpace</span><span class="p">,</span> <span class="n">VectorObservationSpace</span>
|
||||
<span class="kn">from</span> <span class="nn">rl_coach.utils</span> <span class="k">import</span> <span class="n">get_open_port</span><span class="p">,</span> <span class="n">force_list</span>
|
||||
<span class="kn">from</span> <span class="nn">enum</span> <span class="k">import</span> <span class="n">Enum</span>
|
||||
<span class="kn">from</span> <span class="nn">typing</span> <span class="k">import</span> <span class="n">List</span><span class="p">,</span> <span class="n">Union</span>
|
||||
<span class="kn">from</span> <span class="nn">rl_coach.base_parameters</span> <span class="k">import</span> <span class="n">VisualizationParameters</span>
|
||||
<span class="kn">from</span> <span class="nn">rl_coach.filters.filter</span> <span class="k">import</span> <span class="n">InputFilter</span><span class="p">,</span> <span class="n">NoOutputFilter</span>
|
||||
<span class="kn">from</span> <span class="nn">rl_coach.filters.observation.observation_rescale_to_size_filter</span> <span class="k">import</span> <span class="n">ObservationRescaleToSizeFilter</span>
|
||||
<span class="kn">from</span> <span class="nn">rl_coach.filters.observation.observation_stacking_filter</span> <span class="k">import</span> <span class="n">ObservationStackingFilter</span>
|
||||
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
|
||||
|
||||
|
||||
<span class="c1"># enum of the available levels and their path</span>
|
||||
@@ -241,6 +243,7 @@
|
||||
<span class="n">TOWN1</span> <span class="o">=</span> <span class="p">{</span><span class="s2">"map_name"</span><span class="p">:</span> <span class="s2">"Town01"</span><span class="p">,</span> <span class="s2">"map_path"</span><span class="p">:</span> <span class="s2">"/Game/Maps/Town01"</span><span class="p">}</span>
|
||||
<span class="n">TOWN2</span> <span class="o">=</span> <span class="p">{</span><span class="s2">"map_name"</span><span class="p">:</span> <span class="s2">"Town02"</span><span class="p">,</span> <span class="s2">"map_path"</span><span class="p">:</span> <span class="s2">"/Game/Maps/Town02"</span><span class="p">}</span>
|
||||
|
||||
|
||||
<span class="n">key_map</span> <span class="o">=</span> <span class="p">{</span>
|
||||
<span class="s1">'BRAKE'</span><span class="p">:</span> <span class="p">(</span><span class="mi">274</span><span class="p">,),</span> <span class="c1"># down arrow</span>
|
||||
<span class="s1">'GAS'</span><span class="p">:</span> <span class="p">(</span><span class="mi">273</span><span class="p">,),</span> <span class="c1"># up arrow</span>
|
||||
@@ -658,7 +661,7 @@
|
||||
|
||||
<div role="contentinfo">
|
||||
<p>
|
||||
© Copyright 2018, Intel AI Lab
|
||||
© Copyright 2018-2019, Intel AI Lab
|
||||
|
||||
</p>
|
||||
</div>
|
||||
@@ -675,27 +678,16 @@
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
<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>
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
</body>
|
||||
</html>
|
||||
@@ -8,7 +8,7 @@
|
||||
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0">
|
||||
|
||||
<title>rl_coach.environments.control_suite_environment — Reinforcement Learning Coach 0.11.0 documentation</title>
|
||||
<title>rl_coach.environments.control_suite_environment — Reinforcement Learning Coach 0.12.1 documentation</title>
|
||||
|
||||
|
||||
|
||||
@@ -17,9 +17,17 @@
|
||||
|
||||
|
||||
|
||||
<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>
|
||||
|
||||
|
||||
|
||||
@@ -31,10 +39,6 @@
|
||||
<link rel="search" title="Search" href="../../../search.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">
|
||||
@@ -42,10 +46,9 @@
|
||||
|
||||
<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" >
|
||||
|
||||
|
||||
|
||||
@@ -389,7 +392,7 @@
|
||||
|
||||
<div role="contentinfo">
|
||||
<p>
|
||||
© Copyright 2018, Intel AI Lab
|
||||
© Copyright 2018-2019, Intel AI Lab
|
||||
|
||||
</p>
|
||||
</div>
|
||||
@@ -406,27 +409,16 @@
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
<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>
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
</body>
|
||||
</html>
|
||||
@@ -8,7 +8,7 @@
|
||||
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0">
|
||||
|
||||
<title>rl_coach.environments.doom_environment — Reinforcement Learning Coach 0.11.0 documentation</title>
|
||||
<title>rl_coach.environments.doom_environment — Reinforcement Learning Coach 0.12.1 documentation</title>
|
||||
|
||||
|
||||
|
||||
@@ -17,9 +17,17 @@
|
||||
|
||||
|
||||
|
||||
<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>
|
||||
|
||||
|
||||
|
||||
@@ -31,10 +39,6 @@
|
||||
<link rel="search" title="Search" href="../../../search.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">
|
||||
@@ -42,10 +46,9 @@
|
||||
|
||||
<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" >
|
||||
|
||||
|
||||
|
||||
@@ -458,7 +461,7 @@
|
||||
|
||||
<div role="contentinfo">
|
||||
<p>
|
||||
© Copyright 2018, Intel AI Lab
|
||||
© Copyright 2018-2019, Intel AI Lab
|
||||
|
||||
</p>
|
||||
</div>
|
||||
@@ -475,27 +478,16 @@
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
<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>
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
</body>
|
||||
</html>
|
||||
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|
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|
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||||
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<title>rl_coach.filters.observation.observation_move_axis_filter — Reinforcement Learning Coach 0.11.0 documentation</title>
|
||||
<title>rl_coach.filters.observation.observation_move_axis_filter — Reinforcement Learning Coach 0.12.1 documentation</title>
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<title>rl_coach.filters.observation.observation_normalization_filter — Reinforcement Learning Coach 0.11.0 documentation</title>
|
||||
<title>rl_coach.filters.observation.observation_normalization_filter — Reinforcement Learning Coach 0.12.1 documentation</title>
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|
||||
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||||
<title>rl_coach.filters.observation.observation_reduction_by_sub_parts_name_filter — Reinforcement Learning Coach 0.11.0 documentation</title>
|
||||
<title>rl_coach.filters.observation.observation_reduction_by_sub_parts_name_filter — Reinforcement Learning Coach 0.12.1 documentation</title>
|
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||||
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||||
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||||
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|
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© Copyright 2018, Intel AI Lab
|
||||
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|
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|
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|
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@@ -8,7 +8,7 @@
|
||||
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0">
|
||||
|
||||
<title>rl_coach.filters.observation.observation_rescale_size_by_factor_filter — Reinforcement Learning Coach 0.11.0 documentation</title>
|
||||
<title>rl_coach.filters.observation.observation_rescale_size_by_factor_filter — Reinforcement Learning Coach 0.12.1 documentation</title>
|
||||
|
||||
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||||
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|
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|
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
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|
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|
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||||
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||||
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|
||||
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||||
@@ -194,39 +197,25 @@
|
||||
<span class="c1"># limitations under the License.</span>
|
||||
<span class="c1">#</span>
|
||||
|
||||
<span class="kn">from</span> <span class="nn">enum</span> <span class="k">import</span> <span class="n">Enum</span>
|
||||
<span class="kn">from</span> <span class="nn">skimage.transform</span> <span class="k">import</span> <span class="n">resize</span>
|
||||
|
||||
<span class="kn">import</span> <span class="nn">scipy.ndimage</span>
|
||||
|
||||
<span class="kn">from</span> <span class="nn">rl_coach.core_types</span> <span class="k">import</span> <span class="n">ObservationType</span>
|
||||
<span class="kn">from</span> <span class="nn">rl_coach.filters.observation.observation_filter</span> <span class="k">import</span> <span class="n">ObservationFilter</span>
|
||||
<span class="kn">from</span> <span class="nn">rl_coach.spaces</span> <span class="k">import</span> <span class="n">ObservationSpace</span>
|
||||
|
||||
|
||||
<span class="c1"># imresize interpolation types as defined by scipy here:</span>
|
||||
<span class="c1"># https://docs.scipy.org/doc/scipy-0.18.1/reference/generated/scipy.misc.imresize.html</span>
|
||||
<span class="k">class</span> <span class="nc">RescaleInterpolationType</span><span class="p">(</span><span class="n">Enum</span><span class="p">):</span>
|
||||
<span class="n">NEAREST</span> <span class="o">=</span> <span class="s1">'nearest'</span>
|
||||
<span class="n">LANCZOS</span> <span class="o">=</span> <span class="s1">'lanczos'</span>
|
||||
<span class="n">BILINEAR</span> <span class="o">=</span> <span class="s1">'bilinear'</span>
|
||||
<span class="n">BICUBIC</span> <span class="o">=</span> <span class="s1">'bicubic'</span>
|
||||
<span class="n">CUBIC</span> <span class="o">=</span> <span class="s1">'cubic'</span>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="ObservationRescaleSizeByFactorFilter"><a class="viewcode-back" href="../../../../components/filters/input_filters.html#rl_coach.filters.observation.ObservationRescaleSizeByFactorFilter">[docs]</a><span class="k">class</span> <span class="nc">ObservationRescaleSizeByFactorFilter</span><span class="p">(</span><span class="n">ObservationFilter</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Rescales an image observation by some factor. For example, the image size</span>
|
||||
<span class="sd"> can be reduced by a factor of 2.</span>
|
||||
<span class="sd"> Warning: this requires the input observation to be of type uint8 due to scipy requirements!</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">rescale_factor</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span> <span class="n">rescaling_interpolation_type</span><span class="p">:</span> <span class="n">RescaleInterpolationType</span><span class="p">):</span>
|
||||
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">rescale_factor</span><span class="p">:</span> <span class="nb">float</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> :param rescale_factor: the factor by which the observation will be rescaled</span>
|
||||
<span class="sd"> :param rescaling_interpolation_type: the interpolation type for rescaling</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">rescale_factor</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">rescale_factor</span><span class="p">)</span> <span class="c1"># scipy requires float scale factors</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">rescaling_interpolation_type</span> <span class="o">=</span> <span class="n">rescaling_interpolation_type</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">rescale_factor</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">rescale_factor</span><span class="p">)</span>
|
||||
<span class="c1"># TODO: allow selecting the channels dim</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">validate_input_observation_space</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">input_observation_space</span><span class="p">:</span> <span class="n">ObservationSpace</span><span class="p">):</span>
|
||||
@@ -238,13 +227,14 @@
|
||||
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"Observations with 3 dimensions must have 3 channels in the last axis (RGB)"</span><span class="p">)</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">filter</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">observation</span><span class="p">:</span> <span class="n">ObservationType</span><span class="p">,</span> <span class="n">update_internal_state</span><span class="p">:</span> <span class="nb">bool</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="o">-></span> <span class="n">ObservationType</span><span class="p">:</span>
|
||||
<span class="c1"># scipy works only with uint8</span>
|
||||
<span class="n">observation</span> <span class="o">=</span> <span class="n">observation</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">'uint8'</span><span class="p">)</span>
|
||||
<span class="n">rescaled_output_size</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">([</span><span class="nb">int</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">rescale_factor</span> <span class="o">*</span> <span class="n">dim</span><span class="p">)</span> <span class="k">for</span> <span class="n">dim</span> <span class="ow">in</span> <span class="n">observation</span><span class="o">.</span><span class="n">shape</span><span class="p">[:</span><span class="mi">2</span><span class="p">]])</span>
|
||||
|
||||
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">observation</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>
|
||||
<span class="n">rescaled_output_size</span> <span class="o">+=</span> <span class="p">(</span><span class="mi">3</span><span class="p">,)</span>
|
||||
|
||||
<span class="c1"># rescale</span>
|
||||
<span class="n">observation</span> <span class="o">=</span> <span class="n">scipy</span><span class="o">.</span><span class="n">misc</span><span class="o">.</span><span class="n">imresize</span><span class="p">(</span><span class="n">observation</span><span class="p">,</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">rescale_factor</span><span class="p">,</span>
|
||||
<span class="n">interp</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">rescaling_interpolation_type</span><span class="o">.</span><span class="n">value</span><span class="p">)</span>
|
||||
<span class="n">observation</span> <span class="o">=</span> <span class="n">resize</span><span class="p">(</span><span class="n">observation</span><span class="p">,</span> <span class="n">rescaled_output_size</span><span class="p">,</span> <span class="n">anti_aliasing</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">preserve_range</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">'uint8'</span><span class="p">)</span>
|
||||
|
||||
<span class="k">return</span> <span class="n">observation</span>
|
||||
|
||||
@@ -263,7 +253,7 @@
|
||||
|
||||
<div role="contentinfo">
|
||||
<p>
|
||||
© Copyright 2018, Intel AI Lab
|
||||
© Copyright 2018-2019, Intel AI Lab
|
||||
|
||||
</p>
|
||||
</div>
|
||||
@@ -280,27 +270,16 @@
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
<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>
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
</body>
|
||||
</html>
|
||||
+23
-47
@@ -8,7 +8,7 @@
|
||||
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0">
|
||||
|
||||
<title>rl_coach.filters.observation.observation_rescale_to_size_filter — Reinforcement Learning Coach 0.11.0 documentation</title>
|
||||
<title>rl_coach.filters.observation.observation_rescale_to_size_filter — Reinforcement Learning Coach 0.12.1 documentation</title>
|
||||
|
||||
|
||||
|
||||
@@ -17,9 +17,17 @@
|
||||
|
||||
|
||||
|
||||
<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>
|
||||
|
||||
|
||||
|
||||
@@ -31,10 +39,6 @@
|
||||
<link rel="search" title="Search" href="../../../../search.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">
|
||||
@@ -42,10 +46,9 @@
|
||||
|
||||
<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" >
|
||||
|
||||
|
||||
|
||||
@@ -195,41 +198,26 @@
|
||||
<span class="c1">#</span>
|
||||
|
||||
<span class="kn">import</span> <span class="nn">copy</span>
|
||||
<span class="kn">from</span> <span class="nn">enum</span> <span class="k">import</span> <span class="n">Enum</span>
|
||||
|
||||
<span class="kn">from</span> <span class="nn">skimage.transform</span> <span class="k">import</span> <span class="n">resize</span>
|
||||
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
|
||||
<span class="kn">import</span> <span class="nn">scipy.ndimage</span>
|
||||
|
||||
<span class="kn">from</span> <span class="nn">rl_coach.core_types</span> <span class="k">import</span> <span class="n">ObservationType</span>
|
||||
<span class="kn">from</span> <span class="nn">rl_coach.filters.observation.observation_filter</span> <span class="k">import</span> <span class="n">ObservationFilter</span>
|
||||
<span class="kn">from</span> <span class="nn">rl_coach.spaces</span> <span class="k">import</span> <span class="n">ObservationSpace</span><span class="p">,</span> <span class="n">PlanarMapsObservationSpace</span><span class="p">,</span> <span class="n">ImageObservationSpace</span>
|
||||
|
||||
|
||||
<span class="c1"># imresize interpolation types as defined by scipy here:</span>
|
||||
<span class="c1"># https://docs.scipy.org/doc/scipy-0.18.1/reference/generated/scipy.misc.imresize.html</span>
|
||||
<span class="k">class</span> <span class="nc">RescaleInterpolationType</span><span class="p">(</span><span class="n">Enum</span><span class="p">):</span>
|
||||
<span class="n">NEAREST</span> <span class="o">=</span> <span class="s1">'nearest'</span>
|
||||
<span class="n">LANCZOS</span> <span class="o">=</span> <span class="s1">'lanczos'</span>
|
||||
<span class="n">BILINEAR</span> <span class="o">=</span> <span class="s1">'bilinear'</span>
|
||||
<span class="n">BICUBIC</span> <span class="o">=</span> <span class="s1">'bicubic'</span>
|
||||
<span class="n">CUBIC</span> <span class="o">=</span> <span class="s1">'cubic'</span>
|
||||
|
||||
|
||||
<div class="viewcode-block" id="ObservationRescaleToSizeFilter"><a class="viewcode-back" href="../../../../components/filters/input_filters.html#rl_coach.filters.observation.ObservationRescaleToSizeFilter">[docs]</a><span class="k">class</span> <span class="nc">ObservationRescaleToSizeFilter</span><span class="p">(</span><span class="n">ObservationFilter</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Rescales an image observation to a given size. The target size does not</span>
|
||||
<span class="sd"> necessarily keep the aspect ratio of the original observation.</span>
|
||||
<span class="sd"> Warning: this requires the input observation to be of type uint8 due to scipy requirements!</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">output_observation_space</span><span class="p">:</span> <span class="n">PlanarMapsObservationSpace</span><span class="p">,</span>
|
||||
<span class="n">rescaling_interpolation_type</span><span class="p">:</span> <span class="n">RescaleInterpolationType</span><span class="o">=</span><span class="n">RescaleInterpolationType</span><span class="o">.</span><span class="n">BILINEAR</span><span class="p">):</span>
|
||||
<span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">output_observation_space</span><span class="p">:</span> <span class="n">PlanarMapsObservationSpace</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> :param output_observation_space: the output observation space</span>
|
||||
<span class="sd"> :param rescaling_interpolation_type: the interpolation type for rescaling</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">output_observation_space</span> <span class="o">=</span> <span class="n">output_observation_space</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">rescaling_interpolation_type</span> <span class="o">=</span> <span class="n">rescaling_interpolation_type</span>
|
||||
|
||||
<span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">output_observation_space</span><span class="p">,</span> <span class="n">PlanarMapsObservationSpace</span><span class="p">):</span>
|
||||
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s2">"The rescale filter only applies to observation spaces that inherit from "</span>
|
||||
@@ -255,20 +243,19 @@
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">output_observation_space</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">output_observation_space</span><span class="o">.</span><span class="n">channels_axis</span><span class="p">]))</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">filter</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">observation</span><span class="p">:</span> <span class="n">ObservationType</span><span class="p">,</span> <span class="n">update_internal_state</span><span class="p">:</span> <span class="nb">bool</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="o">-></span> <span class="n">ObservationType</span><span class="p">:</span>
|
||||
<span class="c1"># scipy works only with uint8</span>
|
||||
<span class="n">observation</span> <span class="o">=</span> <span class="n">observation</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">'uint8'</span><span class="p">)</span>
|
||||
|
||||
<span class="c1"># rescale</span>
|
||||
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">output_observation_space</span><span class="p">,</span> <span class="n">ImageObservationSpace</span><span class="p">):</span>
|
||||
<span class="n">observation</span> <span class="o">=</span> <span class="n">scipy</span><span class="o">.</span><span class="n">misc</span><span class="o">.</span><span class="n">imresize</span><span class="p">(</span><span class="n">observation</span><span class="p">,</span>
|
||||
<span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">output_observation_space</span><span class="o">.</span><span class="n">shape</span><span class="p">),</span>
|
||||
<span class="n">interp</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">rescaling_interpolation_type</span><span class="o">.</span><span class="n">value</span><span class="p">)</span>
|
||||
<span class="n">observation</span> <span class="o">=</span> <span class="n">resize</span><span class="p">(</span><span class="n">observation</span><span class="p">,</span> <span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">output_observation_space</span><span class="o">.</span><span class="n">shape</span><span class="p">),</span> <span class="n">anti_aliasing</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
|
||||
<span class="n">preserve_range</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">'uint8'</span><span class="p">)</span>
|
||||
|
||||
<span class="k">else</span><span class="p">:</span>
|
||||
<span class="n">new_observation</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">output_observation_space</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">output_observation_space</span><span class="o">.</span><span class="n">channels_axis</span><span class="p">]):</span>
|
||||
<span class="n">new_observation</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">scipy</span><span class="o">.</span><span class="n">misc</span><span class="o">.</span><span class="n">imresize</span><span class="p">(</span><span class="n">observation</span><span class="o">.</span><span class="n">take</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_observation_space</span><span class="o">.</span><span class="n">channels_axis</span><span class="p">),</span>
|
||||
<span class="n">new_observation</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">resize</span><span class="p">(</span><span class="n">observation</span><span class="o">.</span><span class="n">take</span><span class="p">(</span><span class="n">i</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_observation_space</span><span class="o">.</span><span class="n">channels_axis</span><span class="p">),</span>
|
||||
<span class="nb">tuple</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">planar_map_output_shape</span><span class="p">),</span>
|
||||
<span class="n">interp</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">rescaling_interpolation_type</span><span class="o">.</span><span class="n">value</span><span class="p">))</span>
|
||||
<span class="n">preserve_range</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">'uint8'</span><span class="p">))</span>
|
||||
<span class="n">new_observation</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">new_observation</span><span class="p">)</span>
|
||||
<span class="n">observation</span> <span class="o">=</span> <span class="n">new_observation</span><span class="o">.</span><span class="n">swapaxes</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_observation_space</span><span class="o">.</span><span class="n">channels_axis</span><span class="p">)</span>
|
||||
|
||||
@@ -289,7 +276,7 @@
|
||||
|
||||
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|
||||
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||||
<title>rl_coach.filters.reward.reward_normalization_filter — Reinforcement Learning Coach 0.11.0 documentation</title>
|
||||
<title>rl_coach.filters.reward.reward_normalization_filter — Reinforcement Learning Coach 0.12.1 documentation</title>
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||||
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<link href="../../../../_static/css/custom.css" rel="stylesheet" type="text/css">
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© Copyright 2018, Intel AI Lab
|
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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|
||||
<title>rl_coach.filters.reward.reward_rescale_filter — Reinforcement Learning Coach 0.11.0 documentation</title>
|
||||
<title>rl_coach.filters.reward.reward_rescale_filter — Reinforcement Learning Coach 0.12.1 documentation</title>
|
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<link rel="search" title="Search" href="../../../../search.html" />
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<link href="../../../../_static/css/custom.css" rel="stylesheet" type="text/css">
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© Copyright 2018, Intel AI Lab
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© Copyright 2018-2019, Intel AI Lab
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@@ -8,7 +8,7 @@
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||||
<meta name="viewport" content="width=device-width, initial-scale=1.0">
|
||||
|
||||
<title>rl_coach.memories.backend.redis — Reinforcement Learning Coach 0.11.0 documentation</title>
|
||||
<title>rl_coach.memories.backend.redis — Reinforcement Learning Coach 0.12.1 documentation</title>
|
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@@ -17,9 +17,17 @@
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@@ -31,10 +39,6 @@
|
||||
<link rel="search" title="Search" href="../../../../search.html" />
|
||||
<link href="../../../../_static/css/custom.css" rel="stylesheet" type="text/css">
|
||||
|
||||
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||||
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||||
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||||
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||||
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||||
@@ -415,7 +418,7 @@
|
||||
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||||
<div role="contentinfo">
|
||||
<p>
|
||||
© Copyright 2018, Intel AI Lab
|
||||
© Copyright 2018-2019, Intel AI Lab
|
||||
|
||||
</p>
|
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</div>
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||||
@@ -432,27 +435,16 @@
|
||||
|
||||
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||||
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<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>
|
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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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SphinxRtdTheme.Navigation.enable(true);
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|
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|
||||
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|
||||
@@ -8,7 +8,7 @@
|
||||
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0">
|
||||
|
||||
<title>rl_coach.memories.episodic.episodic_experience_replay — Reinforcement Learning Coach 0.11.0 documentation</title>
|
||||
<title>rl_coach.memories.episodic.episodic_experience_replay — Reinforcement Learning Coach 0.12.1 documentation</title>
|
||||
|
||||
|
||||
|
||||
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|
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||||
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||||
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<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>
|
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<script type="text/javascript" src="../../../../_static/underscore.js"></script>
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|
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|
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|
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|
||||
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||||
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|
||||
<link rel="search" title="Search" href="../../../../search.html" />
|
||||
<link href="../../../../_static/css/custom.css" rel="stylesheet" type="text/css">
|
||||
|
||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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|
||||
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||||
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||||
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|
||||
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|
||||
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||||
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|
||||
|
||||
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||||
@@ -195,6 +198,8 @@
|
||||
<span class="c1"># limitations under the License.</span>
|
||||
<span class="c1">#</span>
|
||||
<span class="kn">import</span> <span class="nn">ast</span>
|
||||
<span class="kn">from</span> <span class="nn">copy</span> <span class="k">import</span> <span class="n">deepcopy</span>
|
||||
|
||||
<span class="kn">import</span> <span class="nn">math</span>
|
||||
|
||||
<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
|
||||
@@ -244,6 +249,10 @@
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">last_training_set_episode_id</span> <span class="o">=</span> <span class="kc">None</span> <span class="c1"># used in batch-rl</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">last_training_set_transition_id</span> <span class="o">=</span> <span class="kc">None</span> <span class="c1"># used in batch-rl</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">train_to_eval_ratio</span> <span class="o">=</span> <span class="n">train_to_eval_ratio</span> <span class="c1"># used in batch-rl</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">evaluation_dataset_as_episodes</span> <span class="o">=</span> <span class="kc">None</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">evaluation_dataset_as_transitions</span> <span class="o">=</span> <span class="kc">None</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">frozen</span> <span class="o">=</span> <span class="kc">False</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">length</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">lock</span><span class="p">:</span> <span class="nb">bool</span> <span class="o">=</span> <span class="kc">False</span><span class="p">)</span> <span class="o">-></span> <span class="nb">int</span><span class="p">:</span>
|
||||
<span class="sd">"""</span>
|
||||
@@ -317,10 +326,12 @@
|
||||
<span class="sd"> Shuffle all the episodes in the replay buffer</span>
|
||||
<span class="sd"> :return:</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">assert_not_frozen</span><span class="p">()</span>
|
||||
|
||||
<span class="n">random</span><span class="o">.</span><span class="n">shuffle</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_buffer</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">transitions</span> <span class="o">=</span> <span class="p">[</span><span class="n">t</span> <span class="k">for</span> <span class="n">e</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_buffer</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">e</span><span class="o">.</span><span class="n">transitions</span><span class="p">]</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">get_shuffled_data_generator</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">size</span><span class="p">:</span> <span class="nb">int</span><span class="p">)</span> <span class="o">-></span> <span class="n">List</span><span class="p">[</span><span class="n">Transition</span><span class="p">]:</span>
|
||||
<span class="k">def</span> <span class="nf">get_shuffled_training_data_generator</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">size</span><span class="p">:</span> <span class="nb">int</span><span class="p">)</span> <span class="o">-></span> <span class="n">List</span><span class="p">[</span><span class="n">Transition</span><span class="p">]:</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Get an generator for iterating through the shuffled replay buffer, for processing the data in epochs.</span>
|
||||
<span class="sd"> If the requested size is larger than the number of samples available in the replay buffer then the batch will</span>
|
||||
@@ -331,15 +342,6 @@
|
||||
<span class="sd"> :return: a batch (list) of selected transitions from the replay buffer</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">reader_writer_lock</span><span class="o">.</span><span class="n">lock_writing</span><span class="p">()</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">last_training_set_transition_id</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">train_to_eval_ratio</span> <span class="o"><</span> <span class="mi">0</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">train_to_eval_ratio</span> <span class="o">>=</span> <span class="mi">1</span><span class="p">:</span>
|
||||
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'train_to_eval_ratio should be in the (0, 1] range.'</span><span class="p">)</span>
|
||||
|
||||
<span class="n">transition</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">transitions</span><span class="p">[</span><span class="nb">round</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">train_to_eval_ratio</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_transitions_in_complete_episodes</span><span class="p">())]</span>
|
||||
<span class="n">episode_num</span><span class="p">,</span> <span class="n">episode</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_episode_for_transition</span><span class="p">(</span><span class="n">transition</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">last_training_set_episode_id</span> <span class="o">=</span> <span class="n">episode_num</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">last_training_set_transition_id</span> <span class="o">=</span> \
|
||||
<span class="nb">len</span><span class="p">([</span><span class="n">t</span> <span class="k">for</span> <span class="n">e</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_all_complete_episodes_from_to</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">last_training_set_episode_id</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">e</span><span class="p">])</span>
|
||||
|
||||
<span class="n">shuffled_transition_indices</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">last_training_set_transition_id</span><span class="p">))</span>
|
||||
<span class="n">random</span><span class="o">.</span><span class="n">shuffle</span><span class="p">(</span><span class="n">shuffled_transition_indices</span><span class="p">)</span>
|
||||
@@ -436,6 +438,7 @@
|
||||
<span class="sd"> :param transition: a transition to store</span>
|
||||
<span class="sd"> :return: None</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">assert_not_frozen</span><span class="p">()</span>
|
||||
|
||||
<span class="c1"># Calling super.store() so that in case a memory backend is used, the memory backend can store this transition.</span>
|
||||
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">store</span><span class="p">(</span><span class="n">transition</span><span class="p">)</span>
|
||||
@@ -461,6 +464,8 @@
|
||||
<span class="sd"> :param episode: the new episode to store</span>
|
||||
<span class="sd"> :return: None</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">assert_not_frozen</span><span class="p">()</span>
|
||||
|
||||
<span class="c1"># Calling super.store() so that in case a memory backend is used, the memory backend can store this episode.</span>
|
||||
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">store_episode</span><span class="p">(</span><span class="n">episode</span><span class="p">)</span>
|
||||
|
||||
@@ -502,6 +507,8 @@
|
||||
<span class="sd"> :param episode_index: the index of the episode to remove</span>
|
||||
<span class="sd"> :return: None</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">assert_not_frozen</span><span class="p">()</span>
|
||||
|
||||
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_buffer</span><span class="p">)</span> <span class="o">></span> <span class="n">episode_index</span><span class="p">:</span>
|
||||
<span class="n">episode_length</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_buffer</span><span class="p">[</span><span class="n">episode_index</span><span class="p">]</span><span class="o">.</span><span class="n">length</span><span class="p">()</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_length</span> <span class="o">-=</span> <span class="mi">1</span>
|
||||
@@ -561,6 +568,7 @@
|
||||
<span class="sd"> Clean the memory by removing all the episodes</span>
|
||||
<span class="sd"> :return: None</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">assert_not_frozen</span><span class="p">()</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">reader_writer_lock</span><span class="o">.</span><span class="n">lock_writing_and_reading</span><span class="p">()</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">transitions</span> <span class="o">=</span> <span class="p">[]</span>
|
||||
@@ -589,6 +597,8 @@
|
||||
<span class="sd"> The csv file is assumed to include a list of transitions.</span>
|
||||
<span class="sd"> :param csv_dataset: A construct which holds the dataset parameters</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">assert_not_frozen</span><span class="p">()</span>
|
||||
|
||||
<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="n">csv_dataset</span><span class="o">.</span><span class="n">filepath</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">df</span><span class="p">)</span> <span class="o">></span> <span class="bp">self</span><span class="o">.</span><span class="n">max_size</span><span class="p">[</span><span class="mi">1</span><span class="p">]:</span>
|
||||
<span class="n">screen</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span><span class="s2">"Warning! The number of transitions to load into the replay buffer (</span><span class="si">{}</span><span class="s2">) is "</span>
|
||||
@@ -625,7 +635,58 @@
|
||||
<span class="n">progress_bar</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">episode_ids</span><span class="p">))</span>
|
||||
<span class="n">progress_bar</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">shuffle_episodes</span><span class="p">()</span></div>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">shuffle_episodes</span><span class="p">()</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">freeze</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Freezing the replay buffer does not allow any new transitions to be added to the memory.</span>
|
||||
<span class="sd"> Useful when working with a dataset (e.g. batch-rl or imitation learning).</span>
|
||||
<span class="sd"> :return: None</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">frozen</span> <span class="o">=</span> <span class="kc">True</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">assert_not_frozen</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Check that the memory is not frozen, and can be changed.</span>
|
||||
<span class="sd"> :return:</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">frozen</span> <span class="ow">is</span> <span class="kc">False</span><span class="p">,</span> <span class="s2">"Memory is frozen, and cannot be changed."</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">prepare_evaluation_dataset</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Gather the memory content that will be used for off-policy evaluation in episodes and transitions format</span>
|
||||
<span class="sd"> :return:</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">reader_writer_lock</span><span class="o">.</span><span class="n">lock_writing_and_reading</span><span class="p">()</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">_split_training_and_evaluation_datasets</span><span class="p">()</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">evaluation_dataset_as_episodes</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">get_all_complete_episodes_from_to</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">get_last_training_set_episode_id</span><span class="p">()</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">num_complete_episodes</span><span class="p">()))</span>
|
||||
|
||||
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">evaluation_dataset_as_episodes</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
|
||||
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'train_to_eval_ratio is too high causing the evaluation set to be empty. '</span>
|
||||
<span class="s1">'Consider decreasing its value.'</span><span class="p">)</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">evaluation_dataset_as_transitions</span> <span class="o">=</span> <span class="p">[</span><span class="n">t</span> <span class="k">for</span> <span class="n">e</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">evaluation_dataset_as_episodes</span>
|
||||
<span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">e</span><span class="o">.</span><span class="n">transitions</span><span class="p">]</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">reader_writer_lock</span><span class="o">.</span><span class="n">release_writing_and_reading</span><span class="p">()</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">_split_training_and_evaluation_datasets</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> If the data in the buffer was not split to training and evaluation yet, split it accordingly.</span>
|
||||
<span class="sd"> :return: None</span>
|
||||
<span class="sd"> """</span>
|
||||
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">last_training_set_transition_id</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
|
||||
<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">train_to_eval_ratio</span> <span class="o"><</span> <span class="mi">0</span> <span class="ow">or</span> <span class="bp">self</span><span class="o">.</span><span class="n">train_to_eval_ratio</span> <span class="o">>=</span> <span class="mi">1</span><span class="p">:</span>
|
||||
<span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="s1">'train_to_eval_ratio should be in the (0, 1] range.'</span><span class="p">)</span>
|
||||
|
||||
<span class="n">transition</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">transitions</span><span class="p">[</span><span class="nb">round</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">train_to_eval_ratio</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">num_transitions_in_complete_episodes</span><span class="p">())]</span>
|
||||
<span class="n">episode_num</span><span class="p">,</span> <span class="n">episode</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_episode_for_transition</span><span class="p">(</span><span class="n">transition</span><span class="p">)</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">last_training_set_episode_id</span> <span class="o">=</span> <span class="n">episode_num</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">last_training_set_transition_id</span> <span class="o">=</span> \
|
||||
<span class="nb">len</span><span class="p">([</span><span class="n">t</span> <span class="k">for</span> <span class="n">e</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">get_all_complete_episodes_from_to</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">last_training_set_episode_id</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">e</span><span class="p">])</span></div>
|
||||
</pre></div>
|
||||
|
||||
</div>
|
||||
@@ -638,7 +699,7 @@
|
||||
|
||||
<div role="contentinfo">
|
||||
<p>
|
||||
© Copyright 2018, Intel AI Lab
|
||||
© Copyright 2018-2019, Intel AI Lab
|
||||
|
||||
</p>
|
||||
</div>
|
||||
@@ -655,27 +716,16 @@
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
<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>
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
</body>
|
||||
</html>
|
||||
+16
-24
@@ -8,7 +8,7 @@
|
||||
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0">
|
||||
|
||||
<title>rl_coach.memories.episodic.episodic_hindsight_experience_replay — Reinforcement Learning Coach 0.11.0 documentation</title>
|
||||
<title>rl_coach.memories.episodic.episodic_hindsight_experience_replay — Reinforcement Learning Coach 0.12.1 documentation</title>
|
||||
|
||||
|
||||
|
||||
@@ -17,9 +17,17 @@
|
||||
|
||||
|
||||
|
||||
<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>
|
||||
|
||||
|
||||
|
||||
@@ -31,10 +39,6 @@
|
||||
<link rel="search" title="Search" href="../../../../search.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">
|
||||
@@ -42,10 +46,9 @@
|
||||
|
||||
<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" >
|
||||
|
||||
|
||||
|
||||
@@ -338,7 +341,7 @@
|
||||
|
||||
<div role="contentinfo">
|
||||
<p>
|
||||
© Copyright 2018, Intel AI Lab
|
||||
© Copyright 2018-2019, Intel AI Lab
|
||||
|
||||
</p>
|
||||
</div>
|
||||
@@ -355,27 +358,16 @@
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
<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>
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
</body>
|
||||
</html>
|
||||
+16
-24
@@ -8,7 +8,7 @@
|
||||
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0">
|
||||
|
||||
<title>rl_coach.memories.episodic.episodic_hrl_hindsight_experience_replay — Reinforcement Learning Coach 0.11.0 documentation</title>
|
||||
<title>rl_coach.memories.episodic.episodic_hrl_hindsight_experience_replay — Reinforcement Learning Coach 0.12.1 documentation</title>
|
||||
|
||||
|
||||
|
||||
@@ -17,9 +17,17 @@
|
||||
|
||||
|
||||
|
||||
<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>
|
||||
|
||||
|
||||
|
||||
@@ -31,10 +39,6 @@
|
||||
<link rel="search" title="Search" href="../../../../search.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">
|
||||
@@ -42,10 +46,9 @@
|
||||
|
||||
<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" >
|
||||
|
||||
|
||||
|
||||
@@ -263,7 +266,7 @@
|
||||
|
||||
<div role="contentinfo">
|
||||
<p>
|
||||
© Copyright 2018, Intel AI Lab
|
||||
© Copyright 2018-2019, Intel AI Lab
|
||||
|
||||
</p>
|
||||
</div>
|
||||
@@ -280,27 +283,16 @@
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
<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>
|
||||
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|
||||
|
||||
<title>rl_coach.memories.episodic.single_episode_buffer — Reinforcement Learning Coach 0.11.0 documentation</title>
|
||||
<title>rl_coach.memories.episodic.single_episode_buffer — Reinforcement Learning Coach 0.12.1 documentation</title>
|
||||
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|
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© Copyright 2018, Intel AI Lab
|
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© Copyright 2018-2019, Intel AI Lab
|
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<script type="text/javascript" src="../../../../_static/underscore.js"></script>
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@@ -8,7 +8,7 @@
|
||||
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||||
<meta name="viewport" content="width=device-width, initial-scale=1.0">
|
||||
|
||||
<title>rl_coach.memories.non_episodic.balanced_experience_replay — Reinforcement Learning Coach 0.11.0 documentation</title>
|
||||
<title>rl_coach.memories.non_episodic.balanced_experience_replay — Reinforcement Learning Coach 0.12.1 documentation</title>
|
||||
|
||||
|
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|
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@@ -17,9 +17,17 @@
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<script type="text/javascript" src="../../../../_static/underscore.js"></script>
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<link rel="search" title="Search" href="../../../../search.html" />
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<link href="../../../../_static/css/custom.css" rel="stylesheet" type="text/css">
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@@ -363,7 +366,7 @@
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|
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© Copyright 2018, Intel AI Lab
|
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© Copyright 2018-2019, Intel AI Lab
|
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|
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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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|
||||
+16
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@@ -8,7 +8,7 @@
|
||||
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0">
|
||||
|
||||
<title>rl_coach.memories.non_episodic.differentiable_neural_dictionary — Reinforcement Learning Coach 0.11.0 documentation</title>
|
||||
<title>rl_coach.memories.non_episodic.differentiable_neural_dictionary — Reinforcement Learning Coach 0.12.1 documentation</title>
|
||||
|
||||
|
||||
|
||||
@@ -17,9 +17,17 @@
|
||||
|
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|
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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 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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|
||||
|
||||
|
||||
|
||||
@@ -31,10 +39,6 @@
|
||||
<link rel="search" title="Search" href="../../../../search.html" />
|
||||
<link href="../../../../_static/css/custom.css" rel="stylesheet" type="text/css">
|
||||
|
||||
|
||||
|
||||
<script src="../../../../_static/js/modernizr.min.js"></script>
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||||
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|
||||
|
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|
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|
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|
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|
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|
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|
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|
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|
||||
@@ -486,7 +489,7 @@
|
||||
|
||||
<div role="contentinfo">
|
||||
<p>
|
||||
© Copyright 2018, Intel AI Lab
|
||||
© Copyright 2018-2019, Intel AI Lab
|
||||
|
||||
</p>
|
||||
</div>
|
||||
@@ -503,27 +506,16 @@
|
||||
|
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|
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|
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|
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|
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|
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<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>
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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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SphinxRtdTheme.Navigation.enable(true);
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|
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|
||||
|
||||
|
||||
|
||||
</body>
|
||||
</html>
|
||||
@@ -8,7 +8,7 @@
|
||||
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0">
|
||||
|
||||
<title>rl_coach.memories.non_episodic.experience_replay — Reinforcement Learning Coach 0.11.0 documentation</title>
|
||||
<title>rl_coach.memories.non_episodic.experience_replay — Reinforcement Learning Coach 0.12.1 documentation</title>
|
||||
|
||||
|
||||
|
||||
@@ -17,9 +17,17 @@
|
||||
|
||||
|
||||
|
||||
<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>
|
||||
<script type="text/javascript" src="../../../../_static/jquery.js"></script>
|
||||
<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>
|
||||
|
||||
|
||||
|
||||
@@ -31,10 +39,6 @@
|
||||
<link rel="search" title="Search" href="../../../../search.html" />
|
||||
<link href="../../../../_static/css/custom.css" rel="stylesheet" type="text/css">
|
||||
|
||||
|
||||
|
||||
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|
||||
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||||
|
||||
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|
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|
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|
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<nav data-toggle="wy-nav-shift" class="wy-nav-side">
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|
||||
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|
||||
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|
||||
|
||||
|
||||
|
||||
@@ -234,6 +237,7 @@
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">allow_duplicates_in_batch_sampling</span> <span class="o">=</span> <span class="n">allow_duplicates_in_batch_sampling</span>
|
||||
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">reader_writer_lock</span> <span class="o">=</span> <span class="n">ReaderWriterLock</span><span class="p">()</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">frozen</span> <span class="o">=</span> <span class="kc">False</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">length</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-></span> <span class="nb">int</span><span class="p">:</span>
|
||||
<span class="sd">"""</span>
|
||||
@@ -271,7 +275,7 @@
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">reader_writer_lock</span><span class="o">.</span><span class="n">release_writing</span><span class="p">()</span>
|
||||
<span class="k">return</span> <span class="n">batch</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">get_shuffled_data_generator</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">size</span><span class="p">:</span> <span class="nb">int</span><span class="p">)</span> <span class="o">-></span> <span class="n">List</span><span class="p">[</span><span class="n">Transition</span><span class="p">]:</span>
|
||||
<span class="k">def</span> <span class="nf">get_shuffled_training_data_generator</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">size</span><span class="p">:</span> <span class="nb">int</span><span class="p">)</span> <span class="o">-></span> <span class="n">List</span><span class="p">[</span><span class="n">Transition</span><span class="p">]:</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Get an generator for iterating through the shuffled replay buffer, for processing the data in epochs.</span>
|
||||
<span class="sd"> If the requested size is larger than the number of samples available in the replay buffer then the batch will</span>
|
||||
@@ -315,6 +319,8 @@
|
||||
<span class="sd"> locks and then calls store with lock = True</span>
|
||||
<span class="sd"> :return: None</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">assert_not_frozen</span><span class="p">()</span>
|
||||
|
||||
<span class="c1"># Calling super.store() so that in case a memory backend is used, the memory backend can store this transition.</span>
|
||||
<span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">store</span><span class="p">(</span><span class="n">transition</span><span class="p">)</span>
|
||||
<span class="k">if</span> <span class="n">lock</span><span class="p">:</span>
|
||||
@@ -355,6 +361,8 @@
|
||||
<span class="sd"> :param transition_index: the index of the transition to remove</span>
|
||||
<span class="sd"> :return: None</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">assert_not_frozen</span><span class="p">()</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">lock</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">reader_writer_lock</span><span class="o">.</span><span class="n">lock_writing_and_reading</span><span class="p">()</span>
|
||||
|
||||
@@ -387,6 +395,8 @@
|
||||
<span class="sd"> Clean the memory by removing all the episodes</span>
|
||||
<span class="sd"> :return: None</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">assert_not_frozen</span><span class="p">()</span>
|
||||
|
||||
<span class="k">if</span> <span class="n">lock</span><span class="p">:</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">reader_writer_lock</span><span class="o">.</span><span class="n">lock_writing_and_reading</span><span class="p">()</span>
|
||||
|
||||
@@ -422,6 +432,8 @@
|
||||
<span class="sd"> The pickle file is assumed to include a list of transitions.</span>
|
||||
<span class="sd"> :param file_path: The path to a pickle file to restore</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">assert_not_frozen</span><span class="p">()</span>
|
||||
|
||||
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">file_path</span><span class="p">,</span> <span class="s1">'rb'</span><span class="p">)</span> <span class="k">as</span> <span class="n">file</span><span class="p">:</span>
|
||||
<span class="n">transitions</span> <span class="o">=</span> <span class="n">pickle</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">file</span><span class="p">)</span>
|
||||
<span class="n">num_transitions</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">transitions</span><span class="p">)</span>
|
||||
@@ -438,8 +450,22 @@
|
||||
<span class="k">if</span> <span class="n">transition_idx</span> <span class="o">%</span> <span class="mi">100</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
|
||||
<span class="n">progress_bar</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">transition_idx</span><span class="p">)</span>
|
||||
|
||||
<span class="n">progress_bar</span><span class="o">.</span><span class="n">close</span><span class="p">()</span></div>
|
||||
<span class="n">progress_bar</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">freeze</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Freezing the replay buffer does not allow any new transitions to be added to the memory.</span>
|
||||
<span class="sd"> Useful when working with a dataset (e.g. batch-rl or imitation learning).</span>
|
||||
<span class="sd"> :return: None</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="bp">self</span><span class="o">.</span><span class="n">frozen</span> <span class="o">=</span> <span class="kc">True</span>
|
||||
|
||||
<span class="k">def</span> <span class="nf">assert_not_frozen</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
|
||||
<span class="sd">"""</span>
|
||||
<span class="sd"> Check that the memory is not frozen, and can be changed.</span>
|
||||
<span class="sd"> :return:</span>
|
||||
<span class="sd"> """</span>
|
||||
<span class="k">assert</span> <span class="bp">self</span><span class="o">.</span><span class="n">frozen</span> <span class="ow">is</span> <span class="kc">False</span><span class="p">,</span> <span class="s2">"Memory is frozen, and cannot be changed."</span></div>
|
||||
</pre></div>
|
||||
|
||||
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|
||||
@@ -452,7 +478,7 @@
|
||||
|
||||
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|
||||
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|
||||
© Copyright 2018, Intel AI Lab
|
||||
© Copyright 2018-2019, Intel AI Lab
|
||||
|
||||
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|
||||
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|
||||
@@ -469,27 +495,16 @@
|
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|
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|
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|
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|
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|
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@@ -8,7 +8,7 @@
|
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|
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
|
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|
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<title>rl_coach.memories.non_episodic.prioritized_experience_replay — Reinforcement Learning Coach 0.11.0 documentation</title>
|
||||
<title>rl_coach.memories.non_episodic.prioritized_experience_replay — Reinforcement Learning Coach 0.12.1 documentation</title>
|
||||
|
||||
|
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|
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@@ -17,9 +17,17 @@
|
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@@ -31,10 +39,6 @@
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<link rel="search" title="Search" href="../../../../search.html" />
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<link href="../../../../_static/css/custom.css" rel="stylesheet" type="text/css">
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@@ -489,7 +492,7 @@
|
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|
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|
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<p>
|
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© Copyright 2018, Intel AI Lab
|
||||
© Copyright 2018-2019, Intel AI Lab
|
||||
|
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</p>
|
||||
</div>
|
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@@ -506,27 +509,16 @@
|
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|
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|
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|
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|
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|
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@@ -8,7 +8,7 @@
|
||||
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0">
|
||||
|
||||
<title>rl_coach.memories.non_episodic.transition_collection — Reinforcement Learning Coach 0.11.0 documentation</title>
|
||||
<title>rl_coach.memories.non_episodic.transition_collection — Reinforcement Learning Coach 0.12.1 documentation</title>
|
||||
|
||||
|
||||
|
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@@ -17,9 +17,17 @@
|
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|
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|
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|
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@@ -31,10 +39,6 @@
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<link rel="search" title="Search" href="../../../../search.html" />
|
||||
<link href="../../../../_static/css/custom.css" rel="stylesheet" type="text/css">
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|
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|
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@@ -243,7 +246,7 @@
|
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|
||||
<div role="contentinfo">
|
||||
<p>
|
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© Copyright 2018, Intel AI Lab
|
||||
© Copyright 2018-2019, Intel AI Lab
|
||||
|
||||
</p>
|
||||
</div>
|
||||
@@ -260,27 +263,16 @@
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
||||
@@ -8,7 +8,7 @@
|
||||
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0">
|
||||
|
||||
<title>rl_coach.orchestrators.kubernetes_orchestrator — Reinforcement Learning Coach 0.11.0 documentation</title>
|
||||
<title>rl_coach.orchestrators.kubernetes_orchestrator — Reinforcement Learning Coach 0.12.1 documentation</title>
|
||||
|
||||
|
||||
|
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@@ -17,9 +17,17 @@
|
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|
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<script type="text/javascript" src="../../../_static/js/modernizr.min.js"></script>
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<script type="text/javascript" src="../../../_static/js/theme.js"></script>
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@@ -31,10 +39,6 @@
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<link rel="search" title="Search" href="../../../search.html" />
|
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<link href="../../../_static/css/custom.css" rel="stylesheet" type="text/css">
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|
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@@ -604,7 +607,7 @@
|
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|
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|
||||
<p>
|
||||
© Copyright 2018, Intel AI Lab
|
||||
© Copyright 2018-2019, Intel AI Lab
|
||||
|
||||
</p>
|
||||
</div>
|
||||
@@ -621,27 +624,16 @@
|
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|
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|
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|
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|
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|
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@@ -8,7 +8,7 @@
|
||||
|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0">
|
||||
|
||||
<title>rl_coach.spaces — Reinforcement Learning Coach 0.11.0 documentation</title>
|
||||
<title>rl_coach.spaces — Reinforcement Learning Coach 0.12.1 documentation</title>
|
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|
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|
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|
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|
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© Copyright 2018, Intel AI Lab
|
||||
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|
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|
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|
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|
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@@ -864,27 +867,16 @@
|
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|
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|
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</html>
|
||||
@@ -25,7 +25,9 @@ Blog posts from the Intel® AI website:
|
||||
|
||||
* `Release 0.10.0 <https://ai.intel.com/introducing-reinforcement-learning-coach-0-10-0/)>`_
|
||||
|
||||
* `Release 0.11.0 <https://ai.intel.com/rl-coach-data-science-at-scale/>`_ (current release)
|
||||
* `Release 0.11.0 <https://ai.intel.com/rl-coach-data-science-at-scale/>`_
|
||||
|
||||
* Release 0.12.1 (current release)
|
||||
|
||||
You can find more details in the `GitHub repository <https://github.com/NervanaSystems/coach>`_.
|
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|
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|
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||||
|
||||
@@ -56,6 +58,14 @@ var Search = {
|
||||
_queued_query : null,
|
||||
_pulse_status : -1,
|
||||
|
||||
htmlToText : function(htmlString) {
|
||||
var htmlElement = document.createElement('span');
|
||||
htmlElement.innerHTML = htmlString;
|
||||
$(htmlElement).find('.headerlink').remove();
|
||||
docContent = $(htmlElement).find('[role=main]')[0];
|
||||
return docContent.textContent || docContent.innerText;
|
||||
},
|
||||
|
||||
init : function() {
|
||||
var params = $.getQueryParameters();
|
||||
if (params.q) {
|
||||
@@ -120,7 +130,7 @@ var Search = {
|
||||
this.out = $('#search-results');
|
||||
this.title = $('<h2>' + _('Searching') + '</h2>').appendTo(this.out);
|
||||
this.dots = $('<span></span>').appendTo(this.title);
|
||||
this.status = $('<p style="display: none"></p>').appendTo(this.out);
|
||||
this.status = $('<p class="search-summary"> </p>').appendTo(this.out);
|
||||
this.output = $('<ul class="search"/>').appendTo(this.out);
|
||||
|
||||
$('#search-progress').text(_('Preparing search...'));
|
||||
@@ -259,11 +269,7 @@ var Search = {
|
||||
displayNextItem();
|
||||
});
|
||||
} else if (DOCUMENTATION_OPTIONS.HAS_SOURCE) {
|
||||
var suffix = DOCUMENTATION_OPTIONS.SOURCELINK_SUFFIX;
|
||||
if (suffix === undefined) {
|
||||
suffix = '.txt';
|
||||
}
|
||||
$.ajax({url: DOCUMENTATION_OPTIONS.URL_ROOT + '_sources/' + item[5] + (item[5].slice(-suffix.length) === suffix ? '' : suffix),
|
||||
$.ajax({url: DOCUMENTATION_OPTIONS.URL_ROOT + item[0] + DOCUMENTATION_OPTIONS.FILE_SUFFIX,
|
||||
dataType: "text",
|
||||
complete: function(jqxhr, textstatus) {
|
||||
var data = jqxhr.responseText;
|
||||
@@ -385,6 +391,19 @@ var Search = {
|
||||
{files: terms[word], score: Scorer.term},
|
||||
{files: titleterms[word], score: Scorer.title}
|
||||
];
|
||||
// add support for partial matches
|
||||
if (word.length > 2) {
|
||||
for (var w in terms) {
|
||||
if (w.match(word) && !terms[word]) {
|
||||
_o.push({files: terms[w], score: Scorer.partialTerm})
|
||||
}
|
||||
}
|
||||
for (var w in titleterms) {
|
||||
if (w.match(word) && !titleterms[word]) {
|
||||
_o.push({files: titleterms[w], score: Scorer.partialTitle})
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// no match but word was a required one
|
||||
if ($u.every(_o, function(o){return o.files === undefined;})) {
|
||||
@@ -424,8 +443,12 @@ var Search = {
|
||||
var valid = true;
|
||||
|
||||
// check if all requirements are matched
|
||||
if (fileMap[file].length != searchterms.length)
|
||||
continue;
|
||||
var filteredTermCount = // as search terms with length < 3 are discarded: ignore
|
||||
searchterms.filter(function(term){return term.length > 2}).length
|
||||
if (
|
||||
fileMap[file].length != searchterms.length &&
|
||||
fileMap[file].length != filteredTermCount
|
||||
) continue;
|
||||
|
||||
// ensure that none of the excluded terms is in the search result
|
||||
for (i = 0; i < excluded.length; i++) {
|
||||
@@ -456,7 +479,8 @@ var Search = {
|
||||
* words. the first one is used to find the occurrence, the
|
||||
* latter for highlighting it.
|
||||
*/
|
||||
makeSearchSummary : function(text, keywords, hlwords) {
|
||||
makeSearchSummary : function(htmlText, keywords, hlwords) {
|
||||
var text = Search.htmlToText(htmlText);
|
||||
var textLower = text.toLowerCase();
|
||||
var start = 0;
|
||||
$.each(keywords, function() {
|
||||
|
||||
Vendored
BIN
Binary file not shown.
|
Before Width: | Height: | Size: 214 B |
Vendored
BIN
Binary file not shown.
|
Before Width: | Height: | Size: 203 B |
Vendored
-808
@@ -1,808 +0,0 @@
|
||||
/*
|
||||
* websupport.js
|
||||
* ~~~~~~~~~~~~~
|
||||
*
|
||||
* sphinx.websupport utilities for all documentation.
|
||||
*
|
||||
* :copyright: Copyright 2007-2019 by the Sphinx team, see AUTHORS.
|
||||
* :license: BSD, see LICENSE for details.
|
||||
*
|
||||
*/
|
||||
|
||||
(function($) {
|
||||
$.fn.autogrow = function() {
|
||||
return this.each(function() {
|
||||
var textarea = this;
|
||||
|
||||
$.fn.autogrow.resize(textarea);
|
||||
|
||||
$(textarea)
|
||||
.focus(function() {
|
||||
textarea.interval = setInterval(function() {
|
||||
$.fn.autogrow.resize(textarea);
|
||||
}, 500);
|
||||
})
|
||||
.blur(function() {
|
||||
clearInterval(textarea.interval);
|
||||
});
|
||||
});
|
||||
};
|
||||
|
||||
$.fn.autogrow.resize = function(textarea) {
|
||||
var lineHeight = parseInt($(textarea).css('line-height'), 10);
|
||||
var lines = textarea.value.split('\n');
|
||||
var columns = textarea.cols;
|
||||
var lineCount = 0;
|
||||
$.each(lines, function() {
|
||||
lineCount += Math.ceil(this.length / columns) || 1;
|
||||
});
|
||||
var height = lineHeight * (lineCount + 1);
|
||||
$(textarea).css('height', height);
|
||||
};
|
||||
})(jQuery);
|
||||
|
||||
(function($) {
|
||||
var comp, by;
|
||||
|
||||
function init() {
|
||||
initEvents();
|
||||
initComparator();
|
||||
}
|
||||
|
||||
function initEvents() {
|
||||
$(document).on("click", 'a.comment-close', function(event) {
|
||||
event.preventDefault();
|
||||
hide($(this).attr('id').substring(2));
|
||||
});
|
||||
$(document).on("click", 'a.vote', function(event) {
|
||||
event.preventDefault();
|
||||
handleVote($(this));
|
||||
});
|
||||
$(document).on("click", 'a.reply', function(event) {
|
||||
event.preventDefault();
|
||||
openReply($(this).attr('id').substring(2));
|
||||
});
|
||||
$(document).on("click", 'a.close-reply', function(event) {
|
||||
event.preventDefault();
|
||||
closeReply($(this).attr('id').substring(2));
|
||||
});
|
||||
$(document).on("click", 'a.sort-option', function(event) {
|
||||
event.preventDefault();
|
||||
handleReSort($(this));
|
||||
});
|
||||
$(document).on("click", 'a.show-proposal', function(event) {
|
||||
event.preventDefault();
|
||||
showProposal($(this).attr('id').substring(2));
|
||||
});
|
||||
$(document).on("click", 'a.hide-proposal', function(event) {
|
||||
event.preventDefault();
|
||||
hideProposal($(this).attr('id').substring(2));
|
||||
});
|
||||
$(document).on("click", 'a.show-propose-change', function(event) {
|
||||
event.preventDefault();
|
||||
showProposeChange($(this).attr('id').substring(2));
|
||||
});
|
||||
$(document).on("click", 'a.hide-propose-change', function(event) {
|
||||
event.preventDefault();
|
||||
hideProposeChange($(this).attr('id').substring(2));
|
||||
});
|
||||
$(document).on("click", 'a.accept-comment', function(event) {
|
||||
event.preventDefault();
|
||||
acceptComment($(this).attr('id').substring(2));
|
||||
});
|
||||
$(document).on("click", 'a.delete-comment', function(event) {
|
||||
event.preventDefault();
|
||||
deleteComment($(this).attr('id').substring(2));
|
||||
});
|
||||
$(document).on("click", 'a.comment-markup', function(event) {
|
||||
event.preventDefault();
|
||||
toggleCommentMarkupBox($(this).attr('id').substring(2));
|
||||
});
|
||||
}
|
||||
|
||||
/**
|
||||
* Set comp, which is a comparator function used for sorting and
|
||||
* inserting comments into the list.
|
||||
*/
|
||||
function setComparator() {
|
||||
// If the first three letters are "asc", sort in ascending order
|
||||
// and remove the prefix.
|
||||
if (by.substring(0,3) == 'asc') {
|
||||
var i = by.substring(3);
|
||||
comp = function(a, b) { return a[i] - b[i]; };
|
||||
} else {
|
||||
// Otherwise sort in descending order.
|
||||
comp = function(a, b) { return b[by] - a[by]; };
|
||||
}
|
||||
|
||||
// Reset link styles and format the selected sort option.
|
||||
$('a.sel').attr('href', '#').removeClass('sel');
|
||||
$('a.by' + by).removeAttr('href').addClass('sel');
|
||||
}
|
||||
|
||||
/**
|
||||
* Create a comp function. If the user has preferences stored in
|
||||
* the sortBy cookie, use those, otherwise use the default.
|
||||
*/
|
||||
function initComparator() {
|
||||
by = 'rating'; // Default to sort by rating.
|
||||
// If the sortBy cookie is set, use that instead.
|
||||
if (document.cookie.length > 0) {
|
||||
var start = document.cookie.indexOf('sortBy=');
|
||||
if (start != -1) {
|
||||
start = start + 7;
|
||||
var end = document.cookie.indexOf(";", start);
|
||||
if (end == -1) {
|
||||
end = document.cookie.length;
|
||||
by = unescape(document.cookie.substring(start, end));
|
||||
}
|
||||
}
|
||||
}
|
||||
setComparator();
|
||||
}
|
||||
|
||||
/**
|
||||
* Show a comment div.
|
||||
*/
|
||||
function show(id) {
|
||||
$('#ao' + id).hide();
|
||||
$('#ah' + id).show();
|
||||
var context = $.extend({id: id}, opts);
|
||||
var popup = $(renderTemplate(popupTemplate, context)).hide();
|
||||
popup.find('textarea[name="proposal"]').hide();
|
||||
popup.find('a.by' + by).addClass('sel');
|
||||
var form = popup.find('#cf' + id);
|
||||
form.submit(function(event) {
|
||||
event.preventDefault();
|
||||
addComment(form);
|
||||
});
|
||||
$('#s' + id).after(popup);
|
||||
popup.slideDown('fast', function() {
|
||||
getComments(id);
|
||||
});
|
||||
}
|
||||
|
||||
/**
|
||||
* Hide a comment div.
|
||||
*/
|
||||
function hide(id) {
|
||||
$('#ah' + id).hide();
|
||||
$('#ao' + id).show();
|
||||
var div = $('#sc' + id);
|
||||
div.slideUp('fast', function() {
|
||||
div.remove();
|
||||
});
|
||||
}
|
||||
|
||||
/**
|
||||
* Perform an ajax request to get comments for a node
|
||||
* and insert the comments into the comments tree.
|
||||
*/
|
||||
function getComments(id) {
|
||||
$.ajax({
|
||||
type: 'GET',
|
||||
url: opts.getCommentsURL,
|
||||
data: {node: id},
|
||||
success: function(data, textStatus, request) {
|
||||
var ul = $('#cl' + id);
|
||||
var speed = 100;
|
||||
$('#cf' + id)
|
||||
.find('textarea[name="proposal"]')
|
||||
.data('source', data.source);
|
||||
|
||||
if (data.comments.length === 0) {
|
||||
ul.html('<li>No comments yet.</li>');
|
||||
ul.data('empty', true);
|
||||
} else {
|
||||
// If there are comments, sort them and put them in the list.
|
||||
var comments = sortComments(data.comments);
|
||||
speed = data.comments.length * 100;
|
||||
appendComments(comments, ul);
|
||||
ul.data('empty', false);
|
||||
}
|
||||
$('#cn' + id).slideUp(speed + 200);
|
||||
ul.slideDown(speed);
|
||||
},
|
||||
error: function(request, textStatus, error) {
|
||||
showError('Oops, there was a problem retrieving the comments.');
|
||||
},
|
||||
dataType: 'json'
|
||||
});
|
||||
}
|
||||
|
||||
/**
|
||||
* Add a comment via ajax and insert the comment into the comment tree.
|
||||
*/
|
||||
function addComment(form) {
|
||||
var node_id = form.find('input[name="node"]').val();
|
||||
var parent_id = form.find('input[name="parent"]').val();
|
||||
var text = form.find('textarea[name="comment"]').val();
|
||||
var proposal = form.find('textarea[name="proposal"]').val();
|
||||
|
||||
if (text == '') {
|
||||
showError('Please enter a comment.');
|
||||
return;
|
||||
}
|
||||
|
||||
// Disable the form that is being submitted.
|
||||
form.find('textarea,input').attr('disabled', 'disabled');
|
||||
|
||||
// Send the comment to the server.
|
||||
$.ajax({
|
||||
type: "POST",
|
||||
url: opts.addCommentURL,
|
||||
dataType: 'json',
|
||||
data: {
|
||||
node: node_id,
|
||||
parent: parent_id,
|
||||
text: text,
|
||||
proposal: proposal
|
||||
},
|
||||
success: function(data, textStatus, error) {
|
||||
// Reset the form.
|
||||
if (node_id) {
|
||||
hideProposeChange(node_id);
|
||||
}
|
||||
form.find('textarea')
|
||||
.val('')
|
||||
.add(form.find('input'))
|
||||
.removeAttr('disabled');
|
||||
var ul = $('#cl' + (node_id || parent_id));
|
||||
if (ul.data('empty')) {
|
||||
$(ul).empty();
|
||||
ul.data('empty', false);
|
||||
}
|
||||
insertComment(data.comment);
|
||||
var ao = $('#ao' + node_id);
|
||||
ao.find('img').attr({'src': opts.commentBrightImage});
|
||||
if (node_id) {
|
||||
// if this was a "root" comment, remove the commenting box
|
||||
// (the user can get it back by reopening the comment popup)
|
||||
$('#ca' + node_id).slideUp();
|
||||
}
|
||||
},
|
||||
error: function(request, textStatus, error) {
|
||||
form.find('textarea,input').removeAttr('disabled');
|
||||
showError('Oops, there was a problem adding the comment.');
|
||||
}
|
||||
});
|
||||
}
|
||||
|
||||
/**
|
||||
* Recursively append comments to the main comment list and children
|
||||
* lists, creating the comment tree.
|
||||
*/
|
||||
function appendComments(comments, ul) {
|
||||
$.each(comments, function() {
|
||||
var div = createCommentDiv(this);
|
||||
ul.append($(document.createElement('li')).html(div));
|
||||
appendComments(this.children, div.find('ul.comment-children'));
|
||||
// To avoid stagnating data, don't store the comments children in data.
|
||||
this.children = null;
|
||||
div.data('comment', this);
|
||||
});
|
||||
}
|
||||
|
||||
/**
|
||||
* After adding a new comment, it must be inserted in the correct
|
||||
* location in the comment tree.
|
||||
*/
|
||||
function insertComment(comment) {
|
||||
var div = createCommentDiv(comment);
|
||||
|
||||
// To avoid stagnating data, don't store the comments children in data.
|
||||
comment.children = null;
|
||||
div.data('comment', comment);
|
||||
|
||||
var ul = $('#cl' + (comment.node || comment.parent));
|
||||
var siblings = getChildren(ul);
|
||||
|
||||
var li = $(document.createElement('li'));
|
||||
li.hide();
|
||||
|
||||
// Determine where in the parents children list to insert this comment.
|
||||
for(var i=0; i < siblings.length; i++) {
|
||||
if (comp(comment, siblings[i]) <= 0) {
|
||||
$('#cd' + siblings[i].id)
|
||||
.parent()
|
||||
.before(li.html(div));
|
||||
li.slideDown('fast');
|
||||
return;
|
||||
}
|
||||
}
|
||||
|
||||
// If we get here, this comment rates lower than all the others,
|
||||
// or it is the only comment in the list.
|
||||
ul.append(li.html(div));
|
||||
li.slideDown('fast');
|
||||
}
|
||||
|
||||
function acceptComment(id) {
|
||||
$.ajax({
|
||||
type: 'POST',
|
||||
url: opts.acceptCommentURL,
|
||||
data: {id: id},
|
||||
success: function(data, textStatus, request) {
|
||||
$('#cm' + id).fadeOut('fast');
|
||||
$('#cd' + id).removeClass('moderate');
|
||||
},
|
||||
error: function(request, textStatus, error) {
|
||||
showError('Oops, there was a problem accepting the comment.');
|
||||
}
|
||||
});
|
||||
}
|
||||
|
||||
function deleteComment(id) {
|
||||
$.ajax({
|
||||
type: 'POST',
|
||||
url: opts.deleteCommentURL,
|
||||
data: {id: id},
|
||||
success: function(data, textStatus, request) {
|
||||
var div = $('#cd' + id);
|
||||
if (data == 'delete') {
|
||||
// Moderator mode: remove the comment and all children immediately
|
||||
div.slideUp('fast', function() {
|
||||
div.remove();
|
||||
});
|
||||
return;
|
||||
}
|
||||
// User mode: only mark the comment as deleted
|
||||
div
|
||||
.find('span.user-id:first')
|
||||
.text('[deleted]').end()
|
||||
.find('div.comment-text:first')
|
||||
.text('[deleted]').end()
|
||||
.find('#cm' + id + ', #dc' + id + ', #ac' + id + ', #rc' + id +
|
||||
', #sp' + id + ', #hp' + id + ', #cr' + id + ', #rl' + id)
|
||||
.remove();
|
||||
var comment = div.data('comment');
|
||||
comment.username = '[deleted]';
|
||||
comment.text = '[deleted]';
|
||||
div.data('comment', comment);
|
||||
},
|
||||
error: function(request, textStatus, error) {
|
||||
showError('Oops, there was a problem deleting the comment.');
|
||||
}
|
||||
});
|
||||
}
|
||||
|
||||
function showProposal(id) {
|
||||
$('#sp' + id).hide();
|
||||
$('#hp' + id).show();
|
||||
$('#pr' + id).slideDown('fast');
|
||||
}
|
||||
|
||||
function hideProposal(id) {
|
||||
$('#hp' + id).hide();
|
||||
$('#sp' + id).show();
|
||||
$('#pr' + id).slideUp('fast');
|
||||
}
|
||||
|
||||
function showProposeChange(id) {
|
||||
$('#pc' + id).hide();
|
||||
$('#hc' + id).show();
|
||||
var textarea = $('#pt' + id);
|
||||
textarea.val(textarea.data('source'));
|
||||
$.fn.autogrow.resize(textarea[0]);
|
||||
textarea.slideDown('fast');
|
||||
}
|
||||
|
||||
function hideProposeChange(id) {
|
||||
$('#hc' + id).hide();
|
||||
$('#pc' + id).show();
|
||||
var textarea = $('#pt' + id);
|
||||
textarea.val('').removeAttr('disabled');
|
||||
textarea.slideUp('fast');
|
||||
}
|
||||
|
||||
function toggleCommentMarkupBox(id) {
|
||||
$('#mb' + id).toggle();
|
||||
}
|
||||
|
||||
/** Handle when the user clicks on a sort by link. */
|
||||
function handleReSort(link) {
|
||||
var classes = link.attr('class').split(/\s+/);
|
||||
for (var i=0; i<classes.length; i++) {
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showError("You'll need to login to vote.");
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var d = {
|
||||
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||||
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||||
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|
||||
|
||||
// Swap the vote and unvote links.
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link.hide();
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$('#' + id.charAt(0) + (id.charAt(1) == 'u' ? 'v' : 'u') + d.comment_id)
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||||
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||||
|
||||
// The div the comment is displayed in.
|
||||
var div = $('div#cd' + d.comment_id);
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||||
var data = div.data('comment');
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||||
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||||
// If this is not an unvote, and the other vote arrow has
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// already been pressed, unpress it.
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$('#' + (d.value == 1 ? 'd' : 'u') + 'u' + d.comment_id).hide();
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||||
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||||
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||||
// Update the comments rating in the local data.
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||||
data.rating += (data.vote === 0) ? d.value : (d.value - data.vote);
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||||
data.vote = d.value;
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||||
div.data('comment', data);
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||||
|
||||
// Change the rating text.
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||||
div.find('.rating:first')
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||||
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||||
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||||
// Send the vote information to the server.
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||||
$.ajax({
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||||
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||||
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||||
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showError('Oops, there was a problem casting that vote.');
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||||
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||||
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||||
/**
|
||||
* Open a reply form used to reply to an existing comment.
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||||
*/
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||||
function openReply(id) {
|
||||
// Swap out the reply link for the hide link
|
||||
$('#rl' + id).hide();
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||||
$('#cr' + id).show();
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||||
|
||||
// Add the reply li to the children ul.
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||||
var div = $(renderTemplate(replyTemplate, {id: id})).hide();
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||||
$('#cl' + id)
|
||||
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|
||||
// Setup the submit handler for the reply form.
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||||
.find('#rf' + id)
|
||||
.submit(function(event) {
|
||||
event.preventDefault();
|
||||
addComment($('#rf' + id));
|
||||
closeReply(id);
|
||||
})
|
||||
.find('input[type=button]')
|
||||
.click(function() {
|
||||
closeReply(id);
|
||||
});
|
||||
div.slideDown('fast', function() {
|
||||
$('#rf' + id).find('textarea').focus();
|
||||
});
|
||||
}
|
||||
|
||||
/**
|
||||
* Close the reply form opened with openReply.
|
||||
*/
|
||||
function closeReply(id) {
|
||||
// Remove the reply div from the DOM.
|
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$('#rd' + id).slideUp('fast', function() {
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||||
$(this).remove();
|
||||
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|
||||
|
||||
// Swap out the hide link for the reply link
|
||||
$('#cr' + id).hide();
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||||
$('#rl' + id).show();
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||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
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||||
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|
||||
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|
||||
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||||
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||||
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|
||||
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|
||||
$.each(ph.split('.'), function() {
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
event.preventDefault();
|
||||
show($(this).attr('id').substring(2));
|
||||
})
|
||||
)
|
||||
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|
||||
$(document.createElement('a')).attr({
|
||||
href: '#',
|
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||||
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|
||||
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|
||||
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|
||||
src: opts.closeCommentImage,
|
||||
alt: 'close',
|
||||
title: 'close'
|
||||
}))
|
||||
.click(function(event) {
|
||||
event.preventDefault();
|
||||
hide($(this).attr('id').substring(2));
|
||||
})
|
||||
);
|
||||
});
|
||||
};
|
||||
|
||||
var opts = {
|
||||
processVoteURL: '/_process_vote',
|
||||
addCommentURL: '/_add_comment',
|
||||
getCommentsURL: '/_get_comments',
|
||||
acceptCommentURL: '/_accept_comment',
|
||||
deleteCommentURL: '/_delete_comment',
|
||||
commentImage: '/static/_static/comment.png',
|
||||
closeCommentImage: '/static/_static/comment-close.png',
|
||||
loadingImage: '/static/_static/ajax-loader.gif',
|
||||
commentBrightImage: '/static/_static/comment-bright.png',
|
||||
upArrow: '/static/_static/up.png',
|
||||
downArrow: '/static/_static/down.png',
|
||||
upArrowPressed: '/static/_static/up-pressed.png',
|
||||
downArrowPressed: '/static/_static/down-pressed.png',
|
||||
voting: false,
|
||||
moderator: false
|
||||
};
|
||||
|
||||
if (typeof COMMENT_OPTIONS != "undefined") {
|
||||
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|
||||
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|
||||
|
||||
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|
||||
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|
||||
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|
||||
Sort by:\
|
||||
<a href="#" class="sort-option byrating">best rated</a>\
|
||||
<a href="#" class="sort-option byascage">newest</a>\
|
||||
<a href="#" class="sort-option byage">oldest</a>\
|
||||
</p>\
|
||||
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|
||||
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|
||||
loading comments... <img src="<%loadingImage%>" alt="" /></div>\
|
||||
<ul id="cl<%id%>" class="comment-ul"></ul>\
|
||||
<div id="ca<%id%>">\
|
||||
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|
||||
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|
||||
<div class="comment-markup-box" id="mb<%id%>">\
|
||||
reStructured text markup: <i>*emph*</i>, <b>**strong**</b>, \
|
||||
<code>``code``</code>, \
|
||||
code blocks: <code>::</code> and an indented block after blank line</div>\
|
||||
<form method="post" id="cf<%id%>" class="comment-form" action="">\
|
||||
<textarea name="comment" cols="80"></textarea>\
|
||||
<p class="propose-button">\
|
||||
<a href="#" id="pc<%id%>" class="show-propose-change">\
|
||||
Propose a change ▹\
|
||||
</a>\
|
||||
<a href="#" id="hc<%id%>" class="hide-propose-change">\
|
||||
Propose a change ▿\
|
||||
</a>\
|
||||
</p>\
|
||||
<textarea name="proposal" id="pt<%id%>" cols="80"\
|
||||
spellcheck="false"></textarea>\
|
||||
<input type="submit" value="Add comment" />\
|
||||
<input type="hidden" name="node" value="<%id%>" />\
|
||||
<input type="hidden" name="parent" value="" />\
|
||||
</form>\
|
||||
</div>\
|
||||
</div>';
|
||||
|
||||
var commentTemplate = '\
|
||||
<div id="cd<%id%>" class="sphinx-comment<%css_class%>">\
|
||||
<div class="vote">\
|
||||
<div class="arrow">\
|
||||
<a href="#" id="uv<%id%>" class="vote" title="vote up">\
|
||||
<img src="<%upArrow%>" />\
|
||||
</a>\
|
||||
<a href="#" id="uu<%id%>" class="un vote" title="vote up">\
|
||||
<img src="<%upArrowPressed%>" />\
|
||||
</a>\
|
||||
</div>\
|
||||
<div class="arrow">\
|
||||
<a href="#" id="dv<%id%>" class="vote" title="vote down">\
|
||||
<img src="<%downArrow%>" id="da<%id%>" />\
|
||||
</a>\
|
||||
<a href="#" id="du<%id%>" class="un vote" title="vote down">\
|
||||
<img src="<%downArrowPressed%>" />\
|
||||
</a>\
|
||||
</div>\
|
||||
</div>\
|
||||
<div class="comment-content">\
|
||||
<p class="tagline comment">\
|
||||
<span class="user-id"><%username%></span>\
|
||||
<span class="rating"><%pretty_rating%></span>\
|
||||
<span class="delta"><%time.delta%></span>\
|
||||
</p>\
|
||||
<div class="comment-text comment"><#text#></div>\
|
||||
<p class="comment-opts comment">\
|
||||
<a href="#" class="reply hidden" id="rl<%id%>">reply ▹</a>\
|
||||
<a href="#" class="close-reply" id="cr<%id%>">reply ▿</a>\
|
||||
<a href="#" id="sp<%id%>" class="show-proposal">proposal ▹</a>\
|
||||
<a href="#" id="hp<%id%>" class="hide-proposal">proposal ▿</a>\
|
||||
<a href="#" id="dc<%id%>" class="delete-comment hidden">delete</a>\
|
||||
<span id="cm<%id%>" class="moderation hidden">\
|
||||
<a href="#" id="ac<%id%>" class="accept-comment">accept</a>\
|
||||
</span>\
|
||||
</p>\
|
||||
<pre class="proposal" id="pr<%id%>">\
|
||||
<#proposal_diff#>\
|
||||
</pre>\
|
||||
<ul class="comment-children" id="cl<%id%>"></ul>\
|
||||
</div>\
|
||||
<div class="clearleft"></div>\
|
||||
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||||
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|
||||
|
||||
var replyTemplate = '\
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<li>\
|
||||
<div class="reply-div" id="rd<%id%>">\
|
||||
<form id="rf<%id%>">\
|
||||
<textarea name="comment" cols="80"></textarea>\
|
||||
<input type="submit" value="Add reply" />\
|
||||
<input type="button" value="Cancel" />\
|
||||
<input type="hidden" name="parent" value="<%id%>" />\
|
||||
<input type="hidden" name="node" value="" />\
|
||||
</form>\
|
||||
</div>\
|
||||
</li>';
|
||||
|
||||
$(document).ready(function() {
|
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init();
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|
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// add comment anchors for all paragraphs that are commentable
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@@ -8,7 +8,7 @@
|
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|
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
|
||||
|
||||
<title>Additional Parameters — Reinforcement Learning Coach 0.11.0 documentation</title>
|
||||
<title>Additional Parameters — Reinforcement Learning Coach 0.12.1 documentation</title>
|
||||
|
||||
|
||||
|
||||
@@ -17,9 +17,17 @@
|
||||
|
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</head>
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@@ -193,51 +196,47 @@
|
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<dl class="class">
|
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<dt id="rl_coach.base_parameters.VisualizationParameters">
|
||||
<em class="property">class </em><code class="descclassname">rl_coach.base_parameters.</code><code class="descname">VisualizationParameters</code><span class="sig-paren">(</span><em>print_networks_summary=False</em>, <em>dump_csv=True</em>, <em>dump_signals_to_csv_every_x_episodes=5</em>, <em>dump_gifs=False</em>, <em>dump_mp4=False</em>, <em>video_dump_methods=None</em>, <em>dump_in_episode_signals=False</em>, <em>dump_parameters_documentation=True</em>, <em>render=False</em>, <em>native_rendering=False</em>, <em>max_fps_for_human_control=10</em>, <em>tensorboard=False</em>, <em>add_rendered_image_to_env_response=False</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/rl_coach/base_parameters.html#VisualizationParameters"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#rl_coach.base_parameters.VisualizationParameters" 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>print_networks_summary</strong> – If set to True, a summary of all the networks structure will be printed at the beginning of the experiment</li>
|
||||
<li><strong>dump_csv</strong> – If set to True, the logger will dump logs to a csv file once in every dump_signals_to_csv_every_x_episodes
|
||||
episodes. The logs can be later used to visualize the training process using Coach Dashboard.</li>
|
||||
<li><strong>dump_signals_to_csv_every_x_episodes</strong> – Defines the number of episodes between writing new data to the csv log files. Lower values can affect
|
||||
performance, as writing to disk may take time, and it is done synchronously.</li>
|
||||
<li><strong>dump_gifs</strong> – If set to True, GIF videos of the environment will be stored into the experiment directory according to
|
||||
the filters defined in video_dump_methods.</li>
|
||||
<li><strong>dump_mp4</strong> – If set to True, MP4 videos of the environment will be stored into the experiment directory according to
|
||||
the filters defined in video_dump_methods.</li>
|
||||
<li><strong>dump_in_episode_signals</strong> – If set to True, csv files will be dumped for each episode for inspecting different metrics within the
|
||||
<dd><dl class="field-list simple">
|
||||
<dt class="field-odd">Parameters</dt>
|
||||
<dd class="field-odd"><ul class="simple">
|
||||
<li><p><strong>print_networks_summary</strong> – If set to True, a summary of all the networks structure will be printed at the beginning of the experiment</p></li>
|
||||
<li><p><strong>dump_csv</strong> – If set to True, the logger will dump logs to a csv file once in every dump_signals_to_csv_every_x_episodes
|
||||
episodes. The logs can be later used to visualize the training process using Coach Dashboard.</p></li>
|
||||
<li><p><strong>dump_signals_to_csv_every_x_episodes</strong> – Defines the number of episodes between writing new data to the csv log files. Lower values can affect
|
||||
performance, as writing to disk may take time, and it is done synchronously.</p></li>
|
||||
<li><p><strong>dump_gifs</strong> – If set to True, GIF videos of the environment will be stored into the experiment directory according to
|
||||
the filters defined in video_dump_methods.</p></li>
|
||||
<li><p><strong>dump_mp4</strong> – If set to True, MP4 videos of the environment will be stored into the experiment directory according to
|
||||
the filters defined in video_dump_methods.</p></li>
|
||||
<li><p><strong>dump_in_episode_signals</strong> – If set to True, csv files will be dumped for each episode for inspecting different metrics within the
|
||||
episode. This means that for each step in each episode, different metrics such as the reward, the
|
||||
future return, etc. will be saved. Setting this to True may affect performance severely, and therefore
|
||||
this should be used only for debugging purposes.</li>
|
||||
<li><strong>dump_parameters_documentation</strong> – If set to True, a json file containing all the agent parameters will be saved in the experiment directory.
|
||||
this should be used only for debugging purposes.</p></li>
|
||||
<li><p><strong>dump_parameters_documentation</strong> – If set to True, a json file containing all the agent parameters will be saved in the experiment directory.
|
||||
This may be very useful for inspecting the values defined for each parameters and making sure that all
|
||||
the parameters are defined as expected.</li>
|
||||
<li><strong>render</strong> – If set to True, the environment render function will be called for each step, rendering the image of the
|
||||
the parameters are defined as expected.</p></li>
|
||||
<li><p><strong>render</strong> – If set to True, the environment render function will be called for each step, rendering the image of the
|
||||
environment. This may affect the performance of training, and is highly dependent on the environment.
|
||||
By default, Coach uses PyGame to render the environment image instead of the environment specific rendered.
|
||||
To change this, use the native_rendering flag.</li>
|
||||
<li><strong>native_rendering</strong> – If set to True, the environment native renderer will be used for rendering the environment image.
|
||||
To change this, use the native_rendering flag.</p></li>
|
||||
<li><p><strong>native_rendering</strong> – If set to True, the environment native renderer will be used for rendering the environment image.
|
||||
In some cases this can be slower than rendering using PyGame through Coach, but in other cases the
|
||||
environment opens its native renderer by default, so rendering with PyGame is an unnecessary overhead.</li>
|
||||
<li><strong>max_fps_for_human_control</strong> – The maximum number of frames per second used while playing the environment as a human. This only has
|
||||
effect while using the –play flag for Coach.</li>
|
||||
<li><strong>tensorboard</strong> – If set to True, TensorBoard summaries will be stored in the experiment directory. This can later be
|
||||
loaded in TensorBoard in order to visualize the training process.</li>
|
||||
<li><strong>video_dump_methods</strong> – A list of dump methods that will be used as filters for deciding when to save videos.
|
||||
environment opens its native renderer by default, so rendering with PyGame is an unnecessary overhead.</p></li>
|
||||
<li><p><strong>max_fps_for_human_control</strong> – The maximum number of frames per second used while playing the environment as a human. This only has
|
||||
effect while using the –play flag for Coach.</p></li>
|
||||
<li><p><strong>tensorboard</strong> – If set to True, TensorBoard summaries will be stored in the experiment directory. This can later be
|
||||
loaded in TensorBoard in order to visualize the training process.</p></li>
|
||||
<li><p><strong>video_dump_methods</strong> – A list of dump methods that will be used as filters for deciding when to save videos.
|
||||
The filters in the list will be checked one after the other until the first dump method that returns
|
||||
false for should_dump() in the environment class. This list will only be used if dump_mp4 or dump_gif are
|
||||
set to True.</li>
|
||||
<li><strong>add_rendered_image_to_env_response</strong> – Some environments have a different observation compared to the one displayed while rendering.
|
||||
set to True.</p></li>
|
||||
<li><p><strong>add_rendered_image_to_env_response</strong> – Some environments have a different observation compared to the one displayed while rendering.
|
||||
For some cases it can be useful to pass the rendered image to the agent for visualization purposes.
|
||||
If this flag is set to True, the rendered image will be added to the environment EnvResponse object,
|
||||
which will be passed to the agent and allow using those images.</li>
|
||||
which will be passed to the agent and allow using those images.</p></li>
|
||||
</ul>
|
||||
</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
</dd>
|
||||
</dl>
|
||||
</dd></dl>
|
||||
|
||||
</div>
|
||||
@@ -246,29 +245,25 @@ which will be passed to the agent and allow using those images.</li>
|
||||
<dl class="class">
|
||||
<dt id="rl_coach.base_parameters.PresetValidationParameters">
|
||||
<em class="property">class </em><code class="descclassname">rl_coach.base_parameters.</code><code class="descname">PresetValidationParameters</code><span class="sig-paren">(</span><em>test=False</em>, <em>min_reward_threshold=0</em>, <em>max_episodes_to_achieve_reward=1</em>, <em>num_workers=1</em>, <em>reward_test_level=None</em>, <em>test_using_a_trace_test=True</em>, <em>trace_test_levels=None</em>, <em>trace_max_env_steps=5000</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/rl_coach/base_parameters.html#PresetValidationParameters"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#rl_coach.base_parameters.PresetValidationParameters" 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>test</strong> – A flag which specifies if the preset should be tested as part of the validation process.</li>
|
||||
<li><strong>min_reward_threshold</strong> – The minimum reward that the agent should pass after max_episodes_to_achieve_reward episodes when the
|
||||
preset is run.</li>
|
||||
<li><strong>max_episodes_to_achieve_reward</strong> – The maximum number of episodes that the agent should train using the preset in order to achieve the
|
||||
reward specified by min_reward_threshold.</li>
|
||||
<li><strong>num_workers</strong> – The number of workers that should be used when running this preset in the test suite for validation.</li>
|
||||
<li><strong>reward_test_level</strong> – The environment level or levels, given by a list of strings, that should be tested as part of the
|
||||
reward tests suite.</li>
|
||||
<li><strong>test_using_a_trace_test</strong> – A flag that specifies if the preset should be run as part of the trace tests suite.</li>
|
||||
<li><strong>trace_test_levels</strong> – The environment level or levels, given by a list of strings, that should be tested as part of the
|
||||
trace tests suite.</li>
|
||||
<li><strong>trace_max_env_steps</strong> – An integer representing the maximum number of environment steps to run when running this preset as part
|
||||
of the trace tests suite.</li>
|
||||
<dd><dl class="field-list simple">
|
||||
<dt class="field-odd">Parameters</dt>
|
||||
<dd class="field-odd"><ul class="simple">
|
||||
<li><p><strong>test</strong> – A flag which specifies if the preset should be tested as part of the validation process.</p></li>
|
||||
<li><p><strong>min_reward_threshold</strong> – The minimum reward that the agent should pass after max_episodes_to_achieve_reward episodes when the
|
||||
preset is run.</p></li>
|
||||
<li><p><strong>max_episodes_to_achieve_reward</strong> – The maximum number of episodes that the agent should train using the preset in order to achieve the
|
||||
reward specified by min_reward_threshold.</p></li>
|
||||
<li><p><strong>num_workers</strong> – The number of workers that should be used when running this preset in the test suite for validation.</p></li>
|
||||
<li><p><strong>reward_test_level</strong> – The environment level or levels, given by a list of strings, that should be tested as part of the
|
||||
reward tests suite.</p></li>
|
||||
<li><p><strong>test_using_a_trace_test</strong> – A flag that specifies if the preset should be run as part of the trace tests suite.</p></li>
|
||||
<li><p><strong>trace_test_levels</strong> – The environment level or levels, given by a list of strings, that should be tested as part of the
|
||||
trace tests suite.</p></li>
|
||||
<li><p><strong>trace_max_env_steps</strong> – An integer representing the maximum number of environment steps to run when running this preset as part
|
||||
of the trace tests suite.</p></li>
|
||||
</ul>
|
||||
</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
</dd>
|
||||
</dl>
|
||||
</dd></dl>
|
||||
|
||||
</div>
|
||||
@@ -277,30 +272,26 @@ of the trace tests suite.</li>
|
||||
<dl class="class">
|
||||
<dt id="rl_coach.base_parameters.TaskParameters">
|
||||
<em class="property">class </em><code class="descclassname">rl_coach.base_parameters.</code><code class="descname">TaskParameters</code><span class="sig-paren">(</span><em>framework_type: rl_coach.base_parameters.Frameworks = <Frameworks.tensorflow: 'TensorFlow'></em>, <em>evaluate_only: int = None</em>, <em>use_cpu: bool = False</em>, <em>experiment_path='/tmp'</em>, <em>seed=None</em>, <em>checkpoint_save_secs=None</em>, <em>checkpoint_restore_dir=None</em>, <em>checkpoint_restore_path=None</em>, <em>checkpoint_save_dir=None</em>, <em>export_onnx_graph: bool = False</em>, <em>apply_stop_condition: bool = False</em>, <em>num_gpu: int = 1</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/rl_coach/base_parameters.html#TaskParameters"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#rl_coach.base_parameters.TaskParameters" 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>framework_type</strong> – deep learning framework type. currently only tensorflow is supported</li>
|
||||
<li><strong>evaluate_only</strong> – if not None, the task will be used only for evaluating the model for the given number of steps.
|
||||
A value of 0 means that task will be evaluated for an infinite number of steps.</li>
|
||||
<li><strong>use_cpu</strong> – use the cpu for this task</li>
|
||||
<li><strong>experiment_path</strong> – the path to the directory which will store all the experiment outputs</li>
|
||||
<li><strong>seed</strong> – a seed to use for the random numbers generator</li>
|
||||
<li><strong>checkpoint_save_secs</strong> – the number of seconds between each checkpoint saving</li>
|
||||
<li><strong>checkpoint_restore_dir</strong> – [DEPECRATED - will be removed in one of the next releases - switch to checkpoint_restore_path]
|
||||
the dir to restore the checkpoints from</li>
|
||||
<li><strong>checkpoint_restore_path</strong> – the path to restore the checkpoints from</li>
|
||||
<li><strong>checkpoint_save_dir</strong> – the directory to store the checkpoints in</li>
|
||||
<li><strong>export_onnx_graph</strong> – If set to True, this will export an onnx graph each time a checkpoint is saved</li>
|
||||
<li><strong>apply_stop_condition</strong> – If set to True, this will apply the stop condition defined by reaching a target success rate</li>
|
||||
<li><strong>num_gpu</strong> – number of GPUs to use</li>
|
||||
<dd><dl class="field-list simple">
|
||||
<dt class="field-odd">Parameters</dt>
|
||||
<dd class="field-odd"><ul class="simple">
|
||||
<li><p><strong>framework_type</strong> – deep learning framework type. currently only tensorflow is supported</p></li>
|
||||
<li><p><strong>evaluate_only</strong> – if not None, the task will be used only for evaluating the model for the given number of steps.
|
||||
A value of 0 means that task will be evaluated for an infinite number of steps.</p></li>
|
||||
<li><p><strong>use_cpu</strong> – use the cpu for this task</p></li>
|
||||
<li><p><strong>experiment_path</strong> – the path to the directory which will store all the experiment outputs</p></li>
|
||||
<li><p><strong>seed</strong> – a seed to use for the random numbers generator</p></li>
|
||||
<li><p><strong>checkpoint_save_secs</strong> – the number of seconds between each checkpoint saving</p></li>
|
||||
<li><p><strong>checkpoint_restore_dir</strong> – [DEPECRATED - will be removed in one of the next releases - switch to checkpoint_restore_path]
|
||||
the dir to restore the checkpoints from</p></li>
|
||||
<li><p><strong>checkpoint_restore_path</strong> – the path to restore the checkpoints from</p></li>
|
||||
<li><p><strong>checkpoint_save_dir</strong> – the directory to store the checkpoints in</p></li>
|
||||
<li><p><strong>export_onnx_graph</strong> – If set to True, this will export an onnx graph each time a checkpoint is saved</p></li>
|
||||
<li><p><strong>apply_stop_condition</strong> – If set to True, this will apply the stop condition defined by reaching a target success rate</p></li>
|
||||
<li><p><strong>num_gpu</strong> – number of GPUs to use</p></li>
|
||||
</ul>
|
||||
</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
</dd>
|
||||
</dl>
|
||||
</dd></dl>
|
||||
|
||||
</div>
|
||||
@@ -309,35 +300,31 @@ the dir to restore the checkpoints from</li>
|
||||
<dl class="class">
|
||||
<dt id="rl_coach.base_parameters.DistributedTaskParameters">
|
||||
<em class="property">class </em><code class="descclassname">rl_coach.base_parameters.</code><code class="descname">DistributedTaskParameters</code><span class="sig-paren">(</span><em>framework_type: rl_coach.base_parameters.Frameworks</em>, <em>parameters_server_hosts: str</em>, <em>worker_hosts: str</em>, <em>job_type: str</em>, <em>task_index: int</em>, <em>evaluate_only: int = None</em>, <em>num_tasks: int = None</em>, <em>num_training_tasks: int = None</em>, <em>use_cpu: bool = False</em>, <em>experiment_path=None</em>, <em>dnd=None</em>, <em>shared_memory_scratchpad=None</em>, <em>seed=None</em>, <em>checkpoint_save_secs=None</em>, <em>checkpoint_restore_path=None</em>, <em>checkpoint_save_dir=None</em>, <em>export_onnx_graph: bool = False</em>, <em>apply_stop_condition: bool = False</em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/rl_coach/base_parameters.html#DistributedTaskParameters"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#rl_coach.base_parameters.DistributedTaskParameters" 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>framework_type</strong> – deep learning framework type. currently only tensorflow is supported</li>
|
||||
<li><strong>evaluate_only</strong> – if not None, the task will be used only for evaluating the model for the given number of steps.
|
||||
A value of 0 means that task will be evaluated for an infinite number of steps.</li>
|
||||
<li><strong>parameters_server_hosts</strong> – comma-separated list of hostname:port pairs to which the parameter servers are
|
||||
assigned</li>
|
||||
<li><strong>worker_hosts</strong> – comma-separated list of hostname:port pairs to which the workers are assigned</li>
|
||||
<li><strong>job_type</strong> – the job type - either ps (short for parameters server) or worker</li>
|
||||
<li><strong>task_index</strong> – the index of the process</li>
|
||||
<li><strong>num_tasks</strong> – the number of total tasks that are running (not including the parameters server)</li>
|
||||
<li><strong>num_training_tasks</strong> – the number of tasks that are training (not including the parameters server)</li>
|
||||
<li><strong>use_cpu</strong> – use the cpu for this task</li>
|
||||
<li><strong>experiment_path</strong> – the path to the directory which will store all the experiment outputs</li>
|
||||
<li><strong>dnd</strong> – an external DND to use for NEC. This is a workaround needed for a shared DND not using the scratchpad.</li>
|
||||
<li><strong>seed</strong> – a seed to use for the random numbers generator</li>
|
||||
<li><strong>checkpoint_save_secs</strong> – the number of seconds between each checkpoint saving</li>
|
||||
<li><strong>checkpoint_restore_path</strong> – the path to restore the checkpoints from</li>
|
||||
<li><strong>checkpoint_save_dir</strong> – the directory to store the checkpoints in</li>
|
||||
<li><strong>export_onnx_graph</strong> – If set to True, this will export an onnx graph each time a checkpoint is saved</li>
|
||||
<li><strong>apply_stop_condition</strong> – If set to True, this will apply the stop condition defined by reaching a target success rate</li>
|
||||
<dd><dl class="field-list simple">
|
||||
<dt class="field-odd">Parameters</dt>
|
||||
<dd class="field-odd"><ul class="simple">
|
||||
<li><p><strong>framework_type</strong> – deep learning framework type. currently only tensorflow is supported</p></li>
|
||||
<li><p><strong>evaluate_only</strong> – if not None, the task will be used only for evaluating the model for the given number of steps.
|
||||
A value of 0 means that task will be evaluated for an infinite number of steps.</p></li>
|
||||
<li><p><strong>parameters_server_hosts</strong> – comma-separated list of hostname:port pairs to which the parameter servers are
|
||||
assigned</p></li>
|
||||
<li><p><strong>worker_hosts</strong> – comma-separated list of hostname:port pairs to which the workers are assigned</p></li>
|
||||
<li><p><strong>job_type</strong> – the job type - either ps (short for parameters server) or worker</p></li>
|
||||
<li><p><strong>task_index</strong> – the index of the process</p></li>
|
||||
<li><p><strong>num_tasks</strong> – the number of total tasks that are running (not including the parameters server)</p></li>
|
||||
<li><p><strong>num_training_tasks</strong> – the number of tasks that are training (not including the parameters server)</p></li>
|
||||
<li><p><strong>use_cpu</strong> – use the cpu for this task</p></li>
|
||||
<li><p><strong>experiment_path</strong> – the path to the directory which will store all the experiment outputs</p></li>
|
||||
<li><p><strong>dnd</strong> – an external DND to use for NEC. This is a workaround needed for a shared DND not using the scratchpad.</p></li>
|
||||
<li><p><strong>seed</strong> – a seed to use for the random numbers generator</p></li>
|
||||
<li><p><strong>checkpoint_save_secs</strong> – the number of seconds between each checkpoint saving</p></li>
|
||||
<li><p><strong>checkpoint_restore_path</strong> – the path to restore the checkpoints from</p></li>
|
||||
<li><p><strong>checkpoint_save_dir</strong> – the directory to store the checkpoints in</p></li>
|
||||
<li><p><strong>export_onnx_graph</strong> – If set to True, this will export an onnx graph each time a checkpoint is saved</p></li>
|
||||
<li><p><strong>apply_stop_condition</strong> – If set to True, this will apply the stop condition defined by reaching a target success rate</p></li>
|
||||
</ul>
|
||||
</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
</dd>
|
||||
</dl>
|
||||
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@@ -352,7 +339,7 @@ assigned</li>
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@@ -361,7 +348,7 @@ assigned</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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@@ -378,27 +365,16 @@ assigned</li>
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<title>Behavioral Cloning — Reinforcement Learning Coach 0.11.0 documentation</title>
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<title>Behavioral Cloning — Reinforcement Learning Coach 0.12.1 documentation</title>
|
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@@ -17,9 +17,17 @@
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<link rel="prev" title="ACER" href="../policy_optimization/acer.html" />
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<link href="../../../_static/css/custom.css" rel="stylesheet" type="text/css">
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@@ -230,9 +233,9 @@ These demonstrations are given as state, action tuples, and with no reward.
|
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The training goal is to reduce the difference between the actions predicted by the network and the actions taken by
|
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the expert for each state.</p>
|
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<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>
|
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</ol>
|
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<dl class="class">
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<dt id="rl_coach.agents.bc_agent.BCAlgorithmParameters">
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@@ -254,7 +257,7 @@ the expert for each state.</p>
|
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<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>
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<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>
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@@ -263,7 +266,7 @@ the expert for each state.</p>
|
||||
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||||
<div role="contentinfo">
|
||||
<p>
|
||||
© Copyright 2018, Intel AI Lab
|
||||
© Copyright 2018-2019, Intel AI Lab
|
||||
|
||||
</p>
|
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</div>
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@@ -280,27 +283,16 @@ the expert for each state.</p>
|
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|
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|
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|
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<script type="text/javascript" id="documentation_options" data-url_root="../../../" src="../../../_static/documentation_options.js"></script>
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@@ -8,7 +8,7 @@
|
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|
||||
<meta name="viewport" content="width=device-width, initial-scale=1.0">
|
||||
|
||||
<title>Conditional Imitation Learning — Reinforcement Learning Coach 0.11.0 documentation</title>
|
||||
<title>Conditional Imitation Learning — Reinforcement Learning Coach 0.12.1 documentation</title>
|
||||
|
||||
|
||||
|
||||
@@ -17,9 +17,17 @@
|
||||
|
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|
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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/js/theme.js"></script>
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@@ -33,10 +41,6 @@
|
||||
<link rel="prev" title="Categorical DQN" href="../value_optimization/categorical_dqn.html" />
|
||||
<link href="../../../_static/css/custom.css" rel="stylesheet" type="text/css">
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|
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|
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<script src="../../../_static/js/modernizr.min.js"></script>
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|
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</head>
|
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|
||||
<body class="wy-body-for-nav">
|
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@@ -44,10 +48,9 @@
|
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|
||||
<div class="wy-grid-for-nav">
|
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|
||||
|
||||
<nav data-toggle="wy-nav-shift" class="wy-nav-side">
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||||
<div class="wy-side-scroll">
|
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<div class="wy-side-nav-search">
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<div class="wy-side-nav-search" >
|
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|
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|
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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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||||
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</div>
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||||
@@ -278,7 +278,7 @@ The key of the state dictionary which corresponds to the value that will be used
|
||||
|
||||
<div role="contentinfo">
|
||||
<p>
|
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© Copyright 2018, Intel AI Lab
|
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© Copyright 2018-2019, Intel AI Lab
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|
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</p>
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</div>
|
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@@ -295,27 +295,16 @@ The key of the state dictionary which corresponds to the value that will be used
|
||||
|
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|
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