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Enabling Coach Documentation to be run even when environments are not installed (#326)
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anabwan
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<title>Clipped Proximal Policy Optimization — Reinforcement Learning Coach 0.11.0 documentation</title>
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<title>Clipped Proximal Policy Optimization — Reinforcement Learning Coach 0.12.1 documentation</title>
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<link rel="prev" title="Conditional Imitation Learning" href="../imitation/cil.html" />
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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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<p>Very similar to PPO, with several small (but very simplifying) changes:</p>
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<ol class="arabic">
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<li><p class="first">Train both the value and policy networks, simultaneously, by defining a single loss function,
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which is the sum of each of the networks loss functions. Then, back propagate gradients only once from this unified loss function.</p>
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</li>
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<li><p class="first">The unified network’s optimizer is set to Adam (instead of L-BFGS for the value network as in PPO).</p>
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</li>
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<li><p class="first">Value targets are now also calculated based on the GAE advantages.
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<li><p>Train both the value and policy networks, simultaneously, by defining a single loss function,
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which is the sum of each of the networks loss functions. Then, back propagate gradients only once from this unified loss function.</p></li>
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<li><p>The unified network’s optimizer is set to Adam (instead of L-BFGS for the value network as in PPO).</p></li>
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<li><p>Value targets are now also calculated based on the GAE advantages.
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In this method, the <span class="math notranslate nohighlight">\(V\)</span> values are predicted from the critic network, and then added to the GAE based advantages,
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in order to get a <span class="math notranslate nohighlight">\(Q\)</span> value for each action. Now, since our critic network is predicting a <span class="math notranslate nohighlight">\(V\)</span> value for
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each state, setting the <span class="math notranslate nohighlight">\(Q\)</span> calculated action-values as a target, will on average serve as a <span class="math notranslate nohighlight">\(V\)</span> state-value target.</p>
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</li>
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<li><p class="first">Instead of adapting the penalizing KL divergence coefficient used in PPO, the likelihood ratio
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each state, setting the <span class="math notranslate nohighlight">\(Q\)</span> calculated action-values as a target, will on average serve as a <span class="math notranslate nohighlight">\(V\)</span> state-value target.</p></li>
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<li><p>Instead of adapting the penalizing KL divergence coefficient used in PPO, the likelihood ratio
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<span class="math notranslate nohighlight">\(r_t(\theta) =\frac{\pi_{\theta}(a|s)}{\pi_{\theta_{old}}(a|s)}\)</span> is clipped, to achieve a similar effect.
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This is done by defining the policy’s loss function to be the minimum between the standard surrogate loss and an epsilon
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clipped surrogate loss:</p>
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@@ -253,46 +253,42 @@ clipped surrogate loss:</p>
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<dl class="class">
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<dt id="rl_coach.agents.clipped_ppo_agent.ClippedPPOAlgorithmParameters">
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<em class="property">class </em><code class="descclassname">rl_coach.agents.clipped_ppo_agent.</code><code class="descname">ClippedPPOAlgorithmParameters</code><a class="reference internal" href="../../../_modules/rl_coach/agents/clipped_ppo_agent.html#ClippedPPOAlgorithmParameters"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#rl_coach.agents.clipped_ppo_agent.ClippedPPOAlgorithmParameters" 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>clip_likelihood_ratio_using_epsilon</strong> – (float)
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n-step estimations.</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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<li><strong>optimization_epochs</strong> – (int)
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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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<li><p><strong>optimization_epochs</strong> – (int)
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For each training phase, the collected dataset will be used for multiple epochs, which are defined by the
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optimization_epochs value.</li>
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<li><strong>optimization_epochs</strong> – (Schedule)
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Can be used to define a schedule over the clipping of the likelihood ratio.</li>
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optimization_epochs value.</p></li>
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<li><p><strong>optimization_epochs</strong> – (Schedule)
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Can be used to define a schedule over the clipping of the likelihood ratio.</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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<a href="ddpg.html" class="btn btn-neutral float-right" title="Deep Deterministic Policy Gradient" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right"></span></a>
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<a href="../imitation/cil.html" class="btn btn-neutral" title="Conditional Imitation Learning" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left"></span> Previous</a>
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<a href="../imitation/cil.html" class="btn btn-neutral float-left" title="Conditional Imitation Learning" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left"></span> Previous</a>
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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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