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<div class="section" id="proximal-policy-optimization">
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<h1>Proximal Policy Optimization<a class="headerlink" href="#proximal-policy-optimization" title="Permalink to this headline">¶</a></h1>
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<p><strong>Actions space:</strong> Discrete | Continuous</p>
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<p><strong>References:</strong> <a class="reference external" href="https://arxiv.org/pdf/1707.06347.pdf">Proximal Policy Optimization Algorithms</a></p>
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<div class="section" id="network-structure">
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<h2>Network Structure<a class="headerlink" href="#network-structure" title="Permalink to this headline">¶</a></h2>
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<img alt="../../../_images/ppo.png" class="align-center" src="../../../_images/ppo.png" />
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</div>
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<div class="section" id="algorithm-description">
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<h2>Algorithm Description<a class="headerlink" href="#algorithm-description" title="Permalink to this headline">¶</a></h2>
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<div class="section" id="choosing-an-action-continuous-actions">
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<h3>Choosing an action - Continuous actions<a class="headerlink" href="#choosing-an-action-continuous-actions" title="Permalink to this headline">¶</a></h3>
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<p>Run the observation through the policy network, and get the mean and standard deviation vectors for this observation.
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While in training phase, sample from a multi-dimensional Gaussian distribution with these mean and standard deviation values.
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When testing, just take the mean values predicted by the network.</p>
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</div>
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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><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.</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.</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.</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="sig-prename descclassname">rl_coach.agents.ppo_agent.</code><code class="sig-name 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><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.</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.</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.</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.</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.</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.</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.</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.</p></li>
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</ul>
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</dd>
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</dl>
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</dd></dl>
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