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Enabling Coach Documentation to be run even when environments are not installed (#326)
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anabwan
@@ -8,7 +8,7 @@
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<title>Proximal Policy Optimization — Reinforcement Learning Coach 0.11.0 documentation</title>
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<title>Proximal Policy Optimization — Reinforcement Learning Coach 0.12.1 documentation</title>
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@@ -33,21 +41,16 @@
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<link rel="prev" title="Policy Gradient" href="pg.html" />
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<link href="../../../_static/css/custom.css" rel="stylesheet" type="text/css">
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@@ -234,66 +237,62 @@ When testing, just take the mean values predicted by the network.</p>
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<div class="section" id="training-the-network">
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<h3>Training the network<a class="headerlink" href="#training-the-network" title="Permalink to this headline">¶</a></h3>
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<ol class="arabic simple">
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<li>Collect a big chunk of experience (in the order of thousands of transitions, sampled from multiple episodes).</li>
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<li>Calculate the advantages for each transition, using the <em>Generalized Advantage Estimation</em> method (Schulman ‘2015).</li>
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<li>Run a single training iteration of the value network using an L-BFGS optimizer. Unlike first order optimizers,
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<li><p>Collect a big chunk of experience (in the order of thousands of transitions, sampled from multiple episodes).</p></li>
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<li><p>Calculate the advantages for each transition, using the <em>Generalized Advantage Estimation</em> method (Schulman ‘2015).</p></li>
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<li><p>Run a single training iteration of the value network using an L-BFGS optimizer. Unlike first order optimizers,
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the L-BFGS optimizer runs on the entire dataset at once, without batching.
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It continues running until some low loss threshold is reached. To prevent overfitting to the current dataset,
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the value targets are updated in a soft manner, using an Exponentially Weighted Moving Average, based on the total
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discounted returns of each state in each episode.</li>
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<li>Run several training iterations of the policy network. This is done by using the previously calculated advantages as
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discounted returns of each state in each episode.</p></li>
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<li><p>Run several training iterations of the policy network. This is done by using the previously calculated advantages as
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targets. The loss function penalizes policies that deviate too far from the old policy (the policy that was used <em>before</em>
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starting to run the current set of training iterations) using a regularization term.</li>
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<li>After training is done, the last sampled KL divergence value will be compared with the <em>target KL divergence</em> value,
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starting to run the current set of training iterations) using a regularization term.</p></li>
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<li><p>After training is done, the last sampled KL divergence value will be compared with the <em>target KL divergence</em> value,
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in order to adapt the penalty coefficient used in the policy loss. If the KL divergence went too high,
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increase the penalty, if it went too low, reduce it. Otherwise, leave it unchanged.</li>
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increase the penalty, if it went too low, reduce it. Otherwise, leave it unchanged.</p></li>
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</ol>
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<dl class="class">
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<dt id="rl_coach.agents.ppo_agent.PPOAlgorithmParameters">
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<em class="property">class </em><code class="descclassname">rl_coach.agents.ppo_agent.</code><code class="descname">PPOAlgorithmParameters</code><a class="reference internal" href="../../../_modules/rl_coach/agents/ppo_agent.html#PPOAlgorithmParameters"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#rl_coach.agents.ppo_agent.PPOAlgorithmParameters" title="Permalink to this definition">¶</a></dt>
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<dd><table class="docutils field-list" frame="void" rules="none">
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<col class="field-name" />
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<col class="field-body" />
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<tbody valign="top">
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
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<li><strong>policy_gradient_rescaler</strong> – (PolicyGradientRescaler)
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<dd><dl class="field-list simple">
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<dt class="field-odd">Parameters</dt>
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<dd class="field-odd"><ul class="simple">
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<li><p><strong>policy_gradient_rescaler</strong> – (PolicyGradientRescaler)
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This represents how the critic will be used to update the actor. The critic value function is typically used
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to rescale the gradients calculated by the actor. There are several ways for doing this, such as using the
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advantage of the action, or the generalized advantage estimation (GAE) value.</li>
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<li><strong>gae_lambda</strong> – (float)
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advantage of the action, or the generalized advantage estimation (GAE) value.</p></li>
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<li><p><strong>gae_lambda</strong> – (float)
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The <span class="math notranslate nohighlight">\(\lambda\)</span> value is used within the GAE function in order to weight different bootstrap length
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estimations. Typical values are in the range 0.9-1, and define an exponential decay over the different
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n-step estimations.</li>
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<li><strong>target_kl_divergence</strong> – (float)
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n-step estimations.</p></li>
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<li><p><strong>target_kl_divergence</strong> – (float)
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The target kl divergence between the current policy distribution and the new policy. PPO uses a heuristic to
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bring the KL divergence to this value, by adding a penalty if the kl divergence is higher.</li>
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<li><strong>initial_kl_coefficient</strong> – (float)
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bring the KL divergence to this value, by adding a penalty if the kl divergence is higher.</p></li>
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<li><p><strong>initial_kl_coefficient</strong> – (float)
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The initial weight that will be given to the KL divergence between the current and the new policy in the
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regularization factor.</li>
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<li><strong>high_kl_penalty_coefficient</strong> – (float)
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The penalty that will be given for KL divergence values which are highes than what was defined as the target.</li>
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<li><strong>clip_likelihood_ratio_using_epsilon</strong> – (float)
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regularization factor.</p></li>
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<li><p><strong>high_kl_penalty_coefficient</strong> – (float)
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The penalty that will be given for KL divergence values which are highes than what was defined as the target.</p></li>
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<li><p><strong>clip_likelihood_ratio_using_epsilon</strong> – (float)
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If not None, the likelihood ratio between the current and new policy in the PPO loss function will be
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clipped to the range [1-clip_likelihood_ratio_using_epsilon, 1+clip_likelihood_ratio_using_epsilon].
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This is typically used in the Clipped PPO version of PPO, and should be set to None in regular PPO
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implementations.</li>
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<li><strong>value_targets_mix_fraction</strong> – (float)
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implementations.</p></li>
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<li><p><strong>value_targets_mix_fraction</strong> – (float)
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The targets for the value network are an exponential weighted moving average which uses this mix fraction to
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define how much of the new targets will be taken into account when calculating the loss.
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This value should be set to the range (0,1], where 1 means that only the new targets will be taken into account.</li>
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<li><strong>estimate_state_value_using_gae</strong> – (bool)
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If set to True, the state value will be estimated using the GAE technique.</li>
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<li><strong>use_kl_regularization</strong> – (bool)
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This value should be set to the range (0,1], where 1 means that only the new targets will be taken into account.</p></li>
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<li><p><strong>estimate_state_value_using_gae</strong> – (bool)
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If set to True, the state value will be estimated using the GAE technique.</p></li>
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<li><p><strong>use_kl_regularization</strong> – (bool)
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If set to True, the loss function will be regularized using the KL diveregence between the current and new
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policy, to bound the change of the policy during the network update.</li>
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<li><strong>beta_entropy</strong> – (float)
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policy, to bound the change of the policy during the network update.</p></li>
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<li><p><strong>beta_entropy</strong> – (float)
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An entropy regulaization term can be added to the loss function in order to control exploration. This term
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is weighted using the <span class="math notranslate nohighlight">\(eta\)</span> value defined by beta_entropy.</li>
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is weighted using the <span class="math notranslate nohighlight">\(eta\)</span> value defined by beta_entropy.</p></li>
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</ul>
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</td>
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</tr>
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</tbody>
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</table>
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</dd>
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</dl>
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</dd></dl>
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</div>
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@@ -311,7 +310,7 @@ is weighted using the <span class="math notranslate nohighlight">\(eta\)</span>
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<a href="../value_optimization/rainbow.html" class="btn btn-neutral float-right" title="Rainbow" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right"></span></a>
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<a href="pg.html" class="btn btn-neutral" title="Policy Gradient" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left"></span> Previous</a>
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<a href="pg.html" class="btn btn-neutral float-left" title="Policy Gradient" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left"></span> Previous</a>
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@@ -320,7 +319,7 @@ is weighted using the <span class="math notranslate nohighlight">\(eta\)</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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@@ -337,27 +336,16 @@ is weighted using the <span class="math notranslate nohighlight">\(eta\)</span>
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