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* SAC algorithm * SAC - updates to agent (learn_from_batch), sac_head and sac_q_head to fix problem in gradient calculation. Now SAC agents is able to train. gym_environment - fixing an error in access to gym.spaces * Soft Actor Critic - code cleanup * code cleanup * V-head initialization fix * SAC benchmarks * SAC Documentation * typo fix * documentation fixes * documentation and version update * README typo
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<div class="section" id="deep-deterministic-policy-gradient">
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<h1>Deep Deterministic Policy Gradient<a class="headerlink" href="#deep-deterministic-policy-gradient" title="Permalink to this headline">¶</a></h1>
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<p><strong>Actions space:</strong> Continuous</p>
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<p><strong>References:</strong> <a class="reference external" href="https://arxiv.org/abs/1509.02971">Continuous control with deep 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/ddpg.png" class="align-center" src="../../../_images/ddpg.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">
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<h3>Choosing an action<a class="headerlink" href="#choosing-an-action" title="Permalink to this headline">¶</a></h3>
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<p>Pass the current states through the actor network, and get an action mean vector <span class="math notranslate nohighlight">\(\mu\)</span>.
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While in training phase, use a continuous exploration policy, such as the Ornstein-Uhlenbeck process,
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to add exploration noise to the action. When testing, use the mean vector <span class="math notranslate nohighlight">\(\mu\)</span> as-is.</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>Start by sampling a batch of transitions from the experience replay.</p>
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<ul>
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<li><p class="first">To train the <strong>critic network</strong>, use the following targets:</p>
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<p><span class="math notranslate nohighlight">\(y_t=r(s_t,a_t )+\gamma \cdot Q(s_{t+1},\mu(s_{t+1} ))\)</span></p>
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<p>First run the actor target network, using the next states as the inputs, and get <span class="math notranslate nohighlight">\(\mu (s_{t+1} )\)</span>.
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Next, run the critic target network using the next states and <span class="math notranslate nohighlight">\(\mu (s_{t+1} )\)</span>, and use the output to
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calculate <span class="math notranslate nohighlight">\(y_t\)</span> according to the equation above. To train the network, use the current states and actions
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as the inputs, and <span class="math notranslate nohighlight">\(y_t\)</span> as the targets.</p>
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</li>
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<li><p class="first">To train the <strong>actor network</strong>, use the following equation:</p>
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<p><span class="math notranslate nohighlight">\(\nabla_{\theta^\mu } J \approx E_{s_t \tilde{} \rho^\beta } [\nabla_a Q(s,a)|_{s=s_t,a=\mu (s_t ) } \cdot \nabla_{\theta^\mu} \mu(s)|_{s=s_t} ]\)</span></p>
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<p>Use the actor’s online network to get the action mean values using the current states as the inputs.
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Then, use the critic online network in order to get the gradients of the critic output with respect to the
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action mean values <span class="math notranslate nohighlight">\(\nabla _a Q(s,a)|_{s=s_t,a=\mu(s_t ) }\)</span>.
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Using the chain rule, calculate the gradients of the actor’s output, with respect to the actor weights,
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given <span class="math notranslate nohighlight">\(\nabla_a Q(s,a)\)</span>. Finally, apply those gradients to the actor network.</p>
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</li>
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</ul>
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<p>After every training step, do a soft update of the critic and actor target networks’ weights from the online networks.</p>
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<dl class="class">
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<dt id="rl_coach.agents.ddpg_agent.DDPGAlgorithmParameters">
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<em class="property">class </em><code class="descclassname">rl_coach.agents.ddpg_agent.</code><code class="descname">DDPGAlgorithmParameters</code><a class="reference internal" href="../../../_modules/rl_coach/agents/ddpg_agent.html#DDPGAlgorithmParameters"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#rl_coach.agents.ddpg_agent.DDPGAlgorithmParameters" 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>num_steps_between_copying_online_weights_to_target</strong> – (StepMethod)
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The number of steps between copying the online network weights to the target network weights.</li>
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<li><strong>rate_for_copying_weights_to_target</strong> – (float)
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When copying the online network weights to the target network weights, a soft update will be used, which
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weight the new online network weights by rate_for_copying_weights_to_target</li>
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<li><strong>num_consecutive_playing_steps</strong> – (StepMethod)
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The number of consecutive steps to act between every two training iterations</li>
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<li><strong>use_target_network_for_evaluation</strong> – (bool)
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If set to True, the target network will be used for predicting the actions when choosing actions to act.
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Since the target network weights change more slowly, the predicted actions will be more consistent.</li>
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<li><strong>action_penalty</strong> – (float)
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The amount by which to penalize the network on high action feature (pre-activation) values.
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This can prevent the actions features from saturating the TanH activation function, and therefore prevent the
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gradients from becoming very low.</li>
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<li><strong>clip_critic_targets</strong> – (Tuple[float, float] or None)
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The range to clip the critic target to in order to prevent overestimation of the action values.</li>
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<li><strong>use_non_zero_discount_for_terminal_states</strong> – (bool)
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If set to True, the discount factor will be used for terminal states to bootstrap the next predicted state
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values. If set to False, the terminal states reward will be taken as the target return for the network.</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></dl>
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