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<div class="section" id="policy-gradient">
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<h1>Policy Gradient<a class="headerlink" href="#policy-gradient" 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="http://www-anw.cs.umass.edu/~barto/courses/cs687/williams92simple.pdf">Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning</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/pg.png" class="align-center" src="../../../_images/pg.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-discrete-actions">
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<h3>Choosing an action - Discrete actions<a class="headerlink" href="#choosing-an-action-discrete-actions" title="Permalink to this headline">¶</a></h3>
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<p>Run the current states through the network and get a policy distribution over the actions.
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While training, sample from the policy distribution. When testing, take the action with the highest probability.</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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<p>The policy head loss is defined as <span class="math notranslate nohighlight">\(L=-log (\pi) \cdot PolicyGradientRescaler\)</span>.
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The <code class="code docutils literal notranslate"><span class="pre">PolicyGradientRescaler</span></code> is used in order to reduce the policy gradient variance, which might be very noisy.
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This is done in order to reduce the variance of the updates, since noisy gradient updates might destabilize the policy’s
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convergence. The rescaler is a configurable parameter and there are few options to choose from:</p>
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<ul class="simple">
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<li><strong>Total Episode Return</strong> - The sum of all the discounted rewards during the episode.</li>
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<li><strong>Future Return</strong> - Return from each transition until the end of the episode.</li>
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<li><strong>Future Return Normalized by Episode</strong> - Future returns across the episode normalized by the episode’s mean and standard deviation.</li>
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<li><strong>Future Return Normalized by Timestep</strong> - Future returns normalized using running means and standard deviations,
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which are calculated seperately for each timestep, across different episodes.</li>
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</ul>
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<p>Gradients are accumulated over a number of full played episodes. The gradients accumulation over several episodes
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serves the same purpose - reducing the update variance. After accumulating gradients for several episodes,
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the gradients are then applied to the network.</p>
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<dl class="class">
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<dt id="rl_coach.agents.policy_gradients_agent.PolicyGradientAlgorithmParameters">
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<em class="property">class </em><code class="descclassname">rl_coach.agents.policy_gradients_agent.</code><code class="descname">PolicyGradientAlgorithmParameters</code><a class="reference internal" href="../../../_modules/rl_coach/agents/policy_gradients_agent.html#PolicyGradientAlgorithmParameters"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#rl_coach.agents.policy_gradients_agent.PolicyGradientAlgorithmParameters" 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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The rescaler type to use for the policy gradient loss. For policy gradients, we calculate log probability of
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the action and then multiply it by the policy gradient rescaler. The most basic rescaler is the discounter
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return, but there are other rescalers that are intended for reducing the variance of the updates.</li>
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<li><strong>apply_gradients_every_x_episodes</strong> – (int)
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The number of episodes between applying the accumulated gradients to the network. After every
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num_steps_between_gradient_updates steps, the agent will calculate the gradients for the collected data,
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it will then accumulate it in internal accumulators, and will only apply them to the network once in every
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apply_gradients_every_x_episodes episodes.</li>
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<li><strong>beta_entropy</strong> – (float)
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A factor which defines the amount of entropy regularization to apply to the network. The entropy of the actions
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will be added to the loss and scaled by the given beta factor.</li>
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<li><strong>num_steps_between_gradient_updates</strong> – (int)
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The number of steps between calculating gradients for the collected data. In the A3C paper, this parameter is
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called t_max. Since this algorithm is on-policy, only the steps collected between each two gradient calculations
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are used in the batch.</li>
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</ul>
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</td>
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