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update of api docstrings across coach and tutorials [WIP] (#91)
* updating the documentation website * adding the built docs * update of api docstrings across coach and tutorials 0-2 * added some missing api documentation * New Sphinx based documentation
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Itai Caspi
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<div class="section" id="bootstrapped-dqn">
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<h1>Bootstrapped DQN<a class="headerlink" href="#bootstrapped-dqn" title="Permalink to this headline">¶</a></h1>
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<p><strong>Actions space:</strong> Discrete</p>
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<p><strong>References:</strong> <a class="reference external" href="https://arxiv.org/abs/1602.04621">Deep Exploration via Bootstrapped DQN</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/bs_dqn.png" class="align-center" src="../../../_images/bs_dqn.png" />
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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>The current states are used as the input to the network. The network contains several $Q$ heads, which are used
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for returning different estimations of the action <span class="math notranslate nohighlight">\(Q\)</span> values. For each episode, the bootstrapped exploration policy
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selects a single head to play with during the episode. According to the selected head, only the relevant
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output <span class="math notranslate nohighlight">\(Q\)</span> values are used. Using those <span class="math notranslate nohighlight">\(Q\)</span> values, the exploration policy then selects the action for acting.</p>
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</div>
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<div class="section" id="storing-the-transitions">
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<h3>Storing the transitions<a class="headerlink" href="#storing-the-transitions" title="Permalink to this headline">¶</a></h3>
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<p>For each transition, a Binomial mask is generated according to a predefined probability, and the number of output heads.
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The mask is a binary vector where each element holds a 0 for heads that shouldn’t train on the specific transition,
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and 1 for heads that should use the transition for training. The mask is stored as part of the transition info in
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the replay buffer.</p>
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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>First, sample a batch of transitions from the replay buffer. Run the current states through the network and get the
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current <span class="math notranslate nohighlight">\(Q\)</span> value predictions for all the heads and all the actions. For each transition in the batch,
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and for each output head, if the transition mask is 1 - change the targets of the played action to <span class="math notranslate nohighlight">\(y_t\)</span>,
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according to the standard DQN update rule:</p>
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<p><span class="math notranslate nohighlight">\(y_t=r(s_t,a_t )+\gamma\cdot max_a Q(s_{t+1},a)\)</span></p>
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<p>Otherwise, leave it intact so that the transition does not affect the learning of this head.
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Then, train the online network according to the calculated targets.</p>
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<p>As in DQN, once in every few thousand steps, copy the weights from the online network to the target network.</p>
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<div class="section" id="categorical-dqn">
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<h1>Categorical DQN<a class="headerlink" href="#categorical-dqn" title="Permalink to this headline">¶</a></h1>
|
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<p><strong>Actions space:</strong> Discrete</p>
|
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<p><strong>References:</strong> <a class="reference external" href="https://arxiv.org/abs/1707.06887">A Distributional Perspective on 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/distributional_dqn.png" class="align-center" src="../../../_images/distributional_dqn.png" />
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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="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">
|
||||
<li><p class="first">Sample a batch of transitions from the replay buffer.</p>
|
||||
</li>
|
||||
<li><p class="first">The Bellman update is projected to the set of atoms representing the <span class="math notranslate nohighlight">\(Q\)</span> values distribution, such
|
||||
that the <span class="math notranslate nohighlight">\(i-th\)</span> component of the projected update is calculated as follows:</p>
|
||||
<p><span class="math notranslate nohighlight">\((\Phi \hat{T} Z_{\theta}(s_t,a_t))_i=\sum_{j=0}^{N-1}\Big[1-\frac{\lvert[\hat{T}_{z_{j}}]^{V_{MAX}}_{V_{MIN}}-z_i\rvert}{\Delta z}\Big]^1_0 \ p_j(s_{t+1}, \pi(s_{t+1}))\)</span></p>
|
||||
<p>where:
|
||||
* <span class="math notranslate nohighlight">\([ \cdot ]\)</span> bounds its argument in the range <span class="math notranslate nohighlight">\([a, b]\)</span>
|
||||
* <span class="math notranslate nohighlight">\(\hat{T}_{z_{j}}\)</span> is the Bellman update for atom <span class="math notranslate nohighlight">\(z_j\)</span>: <span class="math notranslate nohighlight">\(\hat{T}_{z_{j}} := r+\gamma z_j\)</span></p>
|
||||
</li>
|
||||
<li><p class="first">Network is trained with the cross entropy loss between the resulting probability distribution and the target
|
||||
probability distribution. Only the target of the actions that were actually taken is updated.</p>
|
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</li>
|
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<li><p class="first">Once in every few thousand steps, weights are copied from the online network to the target network.</p>
|
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</li>
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</ol>
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<dl class="class">
|
||||
<dt id="rl_coach.agents.categorical_dqn_agent.CategoricalDQNAlgorithmParameters">
|
||||
<em class="property">class </em><code class="descclassname">rl_coach.agents.categorical_dqn_agent.</code><code class="descname">CategoricalDQNAlgorithmParameters</code><a class="reference internal" href="../../../_modules/rl_coach/agents/categorical_dqn_agent.html#CategoricalDQNAlgorithmParameters"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#rl_coach.agents.categorical_dqn_agent.CategoricalDQNAlgorithmParameters" 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" />
|
||||
<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">
|
||||
<li><strong>v_min</strong> – (float)
|
||||
The minimal value that will be represented in the network output for predicting the Q value.
|
||||
Corresponds to <span class="math notranslate nohighlight">\(v_{min}\)</span> in the paper.</li>
|
||||
<li><strong>v_max</strong> – (float)
|
||||
The maximum value that will be represented in the network output for predicting the Q value.
|
||||
Corresponds to <span class="math notranslate nohighlight">\(v_{max}\)</span> in the paper.</li>
|
||||
<li><strong>atoms</strong> – (int)
|
||||
The number of atoms that will be used to discretize the range between v_min and v_max.
|
||||
For the C51 algorithm described in the paper, the number of atoms is 51.</li>
|
||||
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<p><strong>Actions space:</strong> Discrete</p>
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<ol class="arabic simple">
|
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<li>Sample a batch of transitions from the replay buffer.</li>
|
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<li>Using the next states from the sampled batch, run the online network in order to find the $Q$ maximizing
|
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action <span class="math notranslate nohighlight">\(argmax_a Q(s_{t+1},a)\)</span>. For these actions, use the corresponding next states and run the target
|
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network to calculate <span class="math notranslate nohighlight">\(Q(s_{t+1},argmax_a Q(s_{t+1},a))\)</span>.</li>
|
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<li>In order to zero out the updates for the actions that were not played (resulting from zeroing the MSE loss),
|
||||
use the current states from the sampled batch, and run the online network to get the current Q values predictions.
|
||||
Set those values as the targets for the actions that were not actually played.</li>
|
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<li>For each action that was played, use the following equation for calculating the targets of the network:
|
||||
<span class="math notranslate nohighlight">\(y_t=r(s_t,a_t )+\gamma \cdot Q(s_{t+1},argmax_a Q(s_{t+1},a))\)</span></li>
|
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<li>Finally, train the online network using the current states as inputs, and with the aforementioned targets.</li>
|
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<h1>Deep Q Networks<a class="headerlink" href="#deep-q-networks" title="Permalink to this headline">¶</a></h1>
|
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<p><strong>Actions space:</strong> Discrete</p>
|
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<p><strong>References:</strong> <a class="reference external" href="https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf">Playing Atari with Deep Reinforcement Learning</a></p>
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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="training-the-network">
|
||||
<h3>Training the network<a class="headerlink" href="#training-the-network" title="Permalink to this headline">¶</a></h3>
|
||||
<ol class="arabic simple">
|
||||
<li>Sample a batch of transitions from the replay buffer.</li>
|
||||
<li>Using the next states from the sampled batch, run the target network to calculate the <span class="math notranslate nohighlight">\(Q\)</span> values for each of
|
||||
the actions <span class="math notranslate nohighlight">\(Q(s_{t+1},a)\)</span>, and keep only the maximum value for each state.</li>
|
||||
<li>In order to zero out the updates for the actions that were not played (resulting from zeroing the MSE loss),
|
||||
use the current states from the sampled batch, and run the online network to get the current Q values predictions.
|
||||
Set those values as the targets for the actions that were not actually played.</li>
|
||||
<li>For each action that was played, use the following equation for calculating the targets of the network: $$ y_t=r(s_t,a_t)+γcdot max_a {Q(s_{t+1},a)} $$
|
||||
<span class="math notranslate nohighlight">\(y_t=r(s_t,a_t )+\gamma \cdot max_a Q(s_{t+1})\)</span></li>
|
||||
<li>Finally, train the online network using the current states as inputs, and with the aforementioned targets.</li>
|
||||
<li>Once in every few thousand steps, copy the weights from the online network to the target network.</li>
|
||||
</ol>
|
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<dl class="class">
|
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<dt id="rl_coach.agents.dqn_agent.DQNAlgorithmParameters">
|
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<em class="property">class </em><code class="descclassname">rl_coach.agents.dqn_agent.</code><code class="descname">DQNAlgorithmParameters</code><a class="reference internal" href="../../../_modules/rl_coach/agents/dqn_agent.html#DQNAlgorithmParameters"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#rl_coach.agents.dqn_agent.DQNAlgorithmParameters" title="Permalink to this definition">¶</a></dt>
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<h1>Dueling DQN<a class="headerlink" href="#dueling-dqn" title="Permalink to this headline">¶</a></h1>
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<p><strong>Actions space:</strong> Discrete</p>
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<p><strong>References:</strong> <a class="reference external" href="https://arxiv.org/abs/1511.06581">Dueling Network Architectures for Deep Reinforcement Learning</a></p>
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<h2>General Description<a class="headerlink" href="#general-description" title="Permalink to this headline">¶</a></h2>
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<p>Dueling DQN presents a change in the network structure comparing to DQN.</p>
|
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<p>Dueling DQN uses a specialized <em>Dueling Q Head</em> in order to separate <span class="math notranslate nohighlight">\(Q\)</span> to an <span class="math notranslate nohighlight">\(A\)</span> (advantage)
|
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stream and a <span class="math notranslate nohighlight">\(V\)</span> stream. Adding this type of structure to the network head allows the network to better differentiate
|
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actions from one another, and significantly improves the learning.</p>
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<p>In many states, the values of the different actions are very similar, and it is less important which action to take.
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This is especially important in environments where there are many actions to choose from. In DQN, on each training
|
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iteration, for each of the states in the batch, we update the <a href="#id1"><span class="problematic" id="id2">:ath:`Q`</span></a> values only for the specific actions taken in
|
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<div class="section" id="mixed-monte-carlo">
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<h1>Mixed Monte Carlo<a class="headerlink" href="#mixed-monte-carlo" title="Permalink to this headline">¶</a></h1>
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<p><strong>Actions space:</strong> Discrete</p>
|
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<p><strong>References:</strong> <a class="reference external" href="https://arxiv.org/abs/1703.01310">Count-Based Exploration with Neural Density Models</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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<h3>Training the network<a class="headerlink" href="#training-the-network" title="Permalink to this headline">¶</a></h3>
|
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<p>In MMC, targets are calculated as a mixture between Double DQN targets and full Monte Carlo samples (total discounted returns).</p>
|
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<p>The DDQN targets are calculated in the same manner as in the DDQN agent:</p>
|
||||
<p><span class="math notranslate nohighlight">\(y_t^{DDQN}=r(s_t,a_t )+\gamma Q(s_{t+1},argmax_a Q(s_{t+1},a))\)</span></p>
|
||||
<p>The Monte Carlo targets are calculated by summing up the discounted rewards across the entire episode:</p>
|
||||
<p><span class="math notranslate nohighlight">\(y_t^{MC}=\sum_{j=0}^T\gamma^j r(s_{t+j},a_{t+j} )\)</span></p>
|
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<p>A mixing ratio $alpha$ is then used to get the final targets:</p>
|
||||
<p><span class="math notranslate nohighlight">\(y_t=(1-\alpha)\cdot y_t^{DDQN}+\alpha \cdot y_t^{MC}\)</span></p>
|
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<p>Finally, the online network is trained using the current states as inputs, and the calculated targets.
|
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Once in every few thousand steps, copy the weights from the online network to the target network.</p>
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<dl class="class">
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<dt id="rl_coach.agents.mmc_agent.MixedMonteCarloAlgorithmParameters">
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<em class="property">class </em><code class="descclassname">rl_coach.agents.mmc_agent.</code><code class="descname">MixedMonteCarloAlgorithmParameters</code><a class="reference internal" href="../../../_modules/rl_coach/agents/mmc_agent.html#MixedMonteCarloAlgorithmParameters"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#rl_coach.agents.mmc_agent.MixedMonteCarloAlgorithmParameters" title="Permalink to this definition">¶</a></dt>
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><strong>monte_carlo_mixing_rate</strong> – (float)
|
||||
The mixing rate is used for setting the amount of monte carlo estimate (full return) that will be mixes into
|
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<h1>N-Step Q Learning<a class="headerlink" href="#n-step-q-learning" title="Permalink to this headline">¶</a></h1>
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<p><strong>Actions space:</strong> Discrete</p>
|
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<p><strong>References:</strong> <a class="reference external" href="https://arxiv.org/abs/1602.01783">Asynchronous Methods for Deep Reinforcement Learning</a></p>
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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 <span class="math notranslate nohighlight">\(N\)</span>-step Q learning algorithm works in similar manner to DQN except for the following changes:</p>
|
||||
<ol class="arabic simple">
|
||||
<li>No replay buffer is used. Instead of sampling random batches of transitions, the network is trained every
|
||||
<span class="math notranslate nohighlight">\(N\)</span> steps using the latest <span class="math notranslate nohighlight">\(N\)</span> steps played by the agent.</li>
|
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<li>In order to stabilize the learning, multiple workers work together to update the network.
|
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This creates the same effect as uncorrelating the samples used for training.</li>
|
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<li>Instead of using single-step Q targets for the network, the rewards from $N$ consequent steps are accumulated
|
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to form the <span class="math notranslate nohighlight">\(N\)</span>-step Q targets, according to the following equation:
|
||||
<span class="math notranslate nohighlight">\(R(s_t, a_t) = \sum_{i=t}^{i=t + k - 1} \gamma^{i-t}r_i +\gamma^{k} V(s_{t+k})\)</span>
|
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where <span class="math notranslate nohighlight">\(k\)</span> is <span class="math notranslate nohighlight">\(T_{max} - State\_Index\)</span> for each state in the batch</li>
|
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<em class="property">class </em><code class="descclassname">rl_coach.agents.n_step_q_agent.</code><code class="descname">NStepQAlgorithmParameters</code><a class="reference internal" href="../../../_modules/rl_coach/agents/n_step_q_agent.html#NStepQAlgorithmParameters"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#rl_coach.agents.n_step_q_agent.NStepQAlgorithmParameters" title="Permalink to this definition">¶</a></dt>
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<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
|
||||
<li><strong>num_steps_between_copying_online_weights_to_target</strong> – (StepMethod)
|
||||
The number of steps between copying the online network weights to the target network weights.</li>
|
||||
<li><strong>apply_gradients_every_x_episodes</strong> – (int)
|
||||
The number of episodes between applying the accumulated gradients to the network. After every
|
||||
num_steps_between_gradient_updates steps, the agent will calculate the gradients for the collected data,
|
||||
it will then accumulate it in internal accumulators, and will only apply them to the network once in every
|
||||
apply_gradients_every_x_episodes episodes.</li>
|
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<li><strong>num_steps_between_gradient_updates</strong> – (int)
|
||||
The number of steps between calculating gradients for the collected data. In the A3C paper, this parameter is
|
||||
called t_max. Since this algorithm is on-policy, only the steps collected between each two gradient calculations
|
||||
are used in the batch.</li>
|
||||
<li><strong>targets_horizon</strong> – (str)
|
||||
Should be either ‘N-Step’ or ‘1-Step’, and defines the length for which to bootstrap the network values over.
|
||||
Essentially, 1-Step follows the regular 1 step bootstrapping Q learning update. For more information,
|
||||
please refer to the original paper (<a class="reference external" href="https://arxiv.org/abs/1602.01783">https://arxiv.org/abs/1602.01783</a>)</li>
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<li class="toctree-l2 current"><a class="current reference internal" href="#">Normalized Advantage Functions</a><ul>
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<div class="section" id="normalized-advantage-functions">
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||||
<h1>Normalized Advantage Functions<a class="headerlink" href="#normalized-advantage-functions" title="Permalink to this headline">¶</a></h1>
|
||||
<p><strong>Actions space:</strong> Continuous</p>
|
||||
<p><strong>References:</strong> <a class="reference external" href="https://arxiv.org/abs/1603.00748.pdf">Continuous Deep Q-Learning with Model-based Acceleration</a></p>
|
||||
<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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<a class="reference internal image-reference" href="../../../_images/naf.png"><img alt="../../../_images/naf.png" class="align-center" src="../../../_images/naf.png" style="width: 600px;" /></a>
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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>
|
||||
<div class="section" id="choosing-an-action">
|
||||
<h3>Choosing an action<a class="headerlink" href="#choosing-an-action" title="Permalink to this headline">¶</a></h3>
|
||||
<p>The current state is used as an input to the network. The action mean <span class="math notranslate nohighlight">\(\mu(s_t )\)</span> is extracted from the output head.
|
||||
It is then passed to the exploration policy which adds noise in order to encourage exploration.</p>
|
||||
</div>
|
||||
<div class="section" id="training-the-network">
|
||||
<h3>Training the network<a class="headerlink" href="#training-the-network" title="Permalink to this headline">¶</a></h3>
|
||||
<p>The network is trained by using the following targets:
|
||||
<span class="math notranslate nohighlight">\(y_t=r(s_t,a_t )+\gamma\cdot V(s_{t+1})\)</span>
|
||||
Use the next states as the inputs to the target network and extract the <span class="math notranslate nohighlight">\(V\)</span> value, from within the head,
|
||||
to get <span class="math notranslate nohighlight">\(V(s_{t+1} )\)</span>. Then, update the online network using the current states and actions as inputs,
|
||||
and <span class="math notranslate nohighlight">\(y_t\)</span> as the targets.
|
||||
After every training step, use a soft update in order to copy the weights from the online network to the target network.</p>
|
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|
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<em class="property">class </em><code class="descclassname">rl_coach.agents.naf_agent.</code><code class="descname">NAFAlgorithmParameters</code><a class="reference internal" href="../../../_modules/rl_coach/agents/naf_agent.html#NAFAlgorithmParameters"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#rl_coach.agents.naf_agent.NAFAlgorithmParameters" title="Permalink to this definition">¶</a></dt>
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<div class="section" id="neural-episodic-control">
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<h1>Neural Episodic Control<a class="headerlink" href="#neural-episodic-control" title="Permalink to this headline">¶</a></h1>
|
||||
<p><strong>Actions space:</strong> Discrete</p>
|
||||
<p><strong>References:</strong> <a class="reference external" href="https://arxiv.org/abs/1703.01988">Neural Episodic Control</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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<a class="reference internal image-reference" href="../../../_images/nec.png"><img alt="../../../_images/nec.png" class="align-center" src="../../../_images/nec.png" style="width: 500px;" /></a>
|
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|
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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>
|
||||
<div class="section" id="choosing-an-action">
|
||||
<h3>Choosing an action<a class="headerlink" href="#choosing-an-action" title="Permalink to this headline">¶</a></h3>
|
||||
<ol class="arabic simple">
|
||||
<li>Use the current state as an input to the online network and extract the state embedding, which is the intermediate
|
||||
output from the middleware.</li>
|
||||
<li>For each possible action <span class="math notranslate nohighlight">\(a_i\)</span>, run the DND head using the state embedding and the selected action <span class="math notranslate nohighlight">\(a_i\)</span> as inputs.
|
||||
The DND is queried and returns the <span class="math notranslate nohighlight">\(P\)</span> nearest neighbor keys and values. The keys and values are used to calculate
|
||||
and return the action <span class="math notranslate nohighlight">\(Q\)</span> value from the network.</li>
|
||||
<li>Pass all the <span class="math notranslate nohighlight">\(Q\)</span> values to the exploration policy and choose an action accordingly.</li>
|
||||
<li>Store the state embeddings and actions taken during the current episode in a small buffer <span class="math notranslate nohighlight">\(B\)</span>, in order to
|
||||
accumulate transitions until it is possible to calculate the total discounted returns over the entire episode.</li>
|
||||
</ol>
|
||||
</div>
|
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<div class="section" id="finalizing-an-episode">
|
||||
<h3>Finalizing an episode<a class="headerlink" href="#finalizing-an-episode" title="Permalink to this headline">¶</a></h3>
|
||||
<p>For each step in the episode, the state embeddings and the taken actions are stored in the buffer <span class="math notranslate nohighlight">\(B\)</span>.
|
||||
When the episode is finished, the replay buffer calculates the <span class="math notranslate nohighlight">\(N\)</span>-step total return of each transition in the
|
||||
buffer, bootstrapped using the maximum <span class="math notranslate nohighlight">\(Q\)</span> value of the <span class="math notranslate nohighlight">\(N\)</span>-th transition. Those values are inserted
|
||||
along with the total return into the DND, and the buffer <span class="math notranslate nohighlight">\(B\)</span> is reset.</p>
|
||||
</div>
|
||||
<div class="section" id="training-the-network">
|
||||
<h3>Training the network<a class="headerlink" href="#training-the-network" title="Permalink to this headline">¶</a></h3>
|
||||
<p>Train the network only when the DND has enough entries for querying.</p>
|
||||
<p>To train the network, the current states are used as the inputs and the <span class="math notranslate nohighlight">\(N\)</span>-step returns are used as the targets.
|
||||
The <span class="math notranslate nohighlight">\(N\)</span>-step return used takes into account <span class="math notranslate nohighlight">\(N\)</span> consecutive steps, and bootstraps the last value from
|
||||
the network if necessary:
|
||||
<span class="math notranslate nohighlight">\(y_t=\sum_{j=0}^{N-1}\gamma^j r(s_{t+j},a_{t+j} ) +\gamma^N max_a Q(s_{t+N},a)\)</span></p>
|
||||
<dl class="class">
|
||||
<dt id="rl_coach.agents.nec_agent.NECAlgorithmParameters">
|
||||
<em class="property">class </em><code class="descclassname">rl_coach.agents.nec_agent.</code><code class="descname">NECAlgorithmParameters</code><a class="reference internal" href="../../../_modules/rl_coach/agents/nec_agent.html#NECAlgorithmParameters"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#rl_coach.agents.nec_agent.NECAlgorithmParameters" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><table class="docutils field-list" frame="void" rules="none">
|
||||
<col class="field-name" />
|
||||
<col class="field-body" />
|
||||
<tbody valign="top">
|
||||
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
|
||||
<li><strong>dnd_size</strong> – (int)
|
||||
Defines the number of transitions that will be stored in each one of the DNDs. Note that the total number
|
||||
of transitions that will be stored is dnd_size x num_actions.</li>
|
||||
<li><strong>l2_norm_added_delta</strong> – (float)
|
||||
A small value that will be added when calculating the weight of each of the DND entries. This follows the
|
||||
<span class="math notranslate nohighlight">\(\delta\)</span> patameter defined in the paper.</li>
|
||||
<li><strong>new_value_shift_coefficient</strong> – (float)
|
||||
In the case where a ew embedding that was added to the DND was already present, the value that will be stored
|
||||
in the DND is a mix between the existing value and the new value. The mix rate is defined by
|
||||
new_value_shift_coefficient.</li>
|
||||
<li><strong>number_of_knn</strong> – (int)
|
||||
The number of neighbors that will be retrieved for each DND query.</li>
|
||||
<li><strong>DND_key_error_threshold</strong> – (float)
|
||||
When the DND is queried for a specific embedding, this threshold will be used to determine if the embedding
|
||||
exists in the DND, since exact matches of embeddings are very rare.</li>
|
||||
<li><strong>propagate_updates_to_DND</strong> – (bool)
|
||||
If set to True, when the gradients of the network will be calculated, the gradients will also be
|
||||
backpropagated through the keys of the DND. The keys will then be updated as well, as if they were regular
|
||||
network weights.</li>
|
||||
<li><strong>n_step</strong> – (int)
|
||||
The bootstrap length that will be used when calculating the state values to store in the DND.</li>
|
||||
<li><strong>bootstrap_total_return_from_old_policy</strong> – (bool)
|
||||
If set to True, the bootstrap that will be used to calculate each state-action value, is the network value
|
||||
when the state was first seen, and not the latest, most up-to-date network value.</li>
|
||||
</ul>
|
||||
</td>
|
||||
</tr>
|
||||
</tbody>
|
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</table>
|
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</dd></dl>
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<div class="section" id="persistent-advantage-learning">
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<h1>Persistent Advantage Learning<a class="headerlink" href="#persistent-advantage-learning" title="Permalink to this headline">¶</a></h1>
|
||||
<p><strong>Actions space:</strong> Discrete</p>
|
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<p><strong>References:</strong> <a class="reference external" href="https://arxiv.org/abs/1512.04860">Increasing the Action Gap: New Operators for 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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<h2>Algorithm Description<a class="headerlink" href="#algorithm-description" title="Permalink to this headline">¶</a></h2>
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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>
|
||||
<ol class="arabic simple">
|
||||
<li>Sample a batch of transitions from the replay buffer.</li>
|
||||
<li>Start by calculating the initial target values in the same manner as they are calculated in DDQN
|
||||
<span class="math notranslate nohighlight">\(y_t^{DDQN}=r(s_t,a_t )+\gamma Q(s_{t+1},argmax_a Q(s_{t+1},a))\)</span></li>
|
||||
<li>The action gap <span class="math notranslate nohighlight">\(V(s_t )-Q(s_t,a_t)\)</span> should then be subtracted from each of the calculated targets.
|
||||
To calculate the action gap, run the target network using the current states and get the <span class="math notranslate nohighlight">\(Q\)</span> values
|
||||
for all the actions. Then estimate <span class="math notranslate nohighlight">\(V\)</span> as the maximum predicted <span class="math notranslate nohighlight">\(Q\)</span> value for the current state:
|
||||
<span class="math notranslate nohighlight">\(V(s_t )=max_a Q(s_t,a)\)</span></li>
|
||||
<li>For <em>advantage learning (AL)</em>, reduce the action gap weighted by a predefined parameter <span class="math notranslate nohighlight">\(\alpha\)</span> from
|
||||
the targets <span class="math notranslate nohighlight">\(y_t^{DDQN}\)</span>:
|
||||
<span class="math notranslate nohighlight">\(y_t=y_t^{DDQN}-\alpha \cdot (V(s_t )-Q(s_t,a_t ))\)</span></li>
|
||||
<li>For <em>persistent advantage learning (PAL)</em>, the target network is also used in order to calculate the action
|
||||
gap for the next state:
|
||||
<span class="math notranslate nohighlight">\(V(s_{t+1} )-Q(s_{t+1},a_{t+1})\)</span>
|
||||
where <span class="math notranslate nohighlight">\(a_{t+1}\)</span> is chosen by running the next states through the online network and choosing the action that
|
||||
has the highest predicted <span class="math notranslate nohighlight">\(Q\)</span> value. Finally, the targets will be defined as -
|
||||
<span class="math notranslate nohighlight">\(y_t=y_t^{DDQN}-\alpha \cdot min(V(s_t )-Q(s_t,a_t ),V(s_{t+1} )-Q(s_{t+1},a_{t+1} ))\)</span></li>
|
||||
<li>Train the online network using the current states as inputs, and with the aforementioned targets.</li>
|
||||
<li>Once in every few thousand steps, copy the weights from the online network to the target network.</li>
|
||||
</ol>
|
||||
<dl class="class">
|
||||
<dt id="rl_coach.agents.pal_agent.PALAlgorithmParameters">
|
||||
<em class="property">class </em><code class="descclassname">rl_coach.agents.pal_agent.</code><code class="descname">PALAlgorithmParameters</code><a class="reference internal" href="../../../_modules/rl_coach/agents/pal_agent.html#PALAlgorithmParameters"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#rl_coach.agents.pal_agent.PALAlgorithmParameters" 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-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">
|
||||
<li><strong>pal_alpha</strong> – (float)
|
||||
A factor that weights the amount by which the advantage learning update will be taken into account.</li>
|
||||
<li><strong>persistent_advantage_learning</strong> – (bool)
|
||||
If set to True, the persistent mode of advantage learning will be used, which encourages the agent to take
|
||||
the same actions one after the other instead of changing actions.</li>
|
||||
<li><strong>monte_carlo_mixing_rate</strong> – (float)
|
||||
The amount of monte carlo values to mix into the targets of the network. The monte carlo values are just the
|
||||
total discounted returns, and they can help reduce the time it takes for the network to update to the newly
|
||||
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<div class="section" id="quantile-regression-dqn">
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<h1>Quantile Regression DQN<a class="headerlink" href="#quantile-regression-dqn" title="Permalink to this headline">¶</a></h1>
|
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<p><strong>Actions space:</strong> Discrete</p>
|
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<p><strong>References:</strong> <a class="reference external" href="https://arxiv.org/abs/1710.10044">Distributional Reinforcement Learning with Quantile Regression</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/qr_dqn.png" class="align-center" src="../../../_images/qr_dqn.png" />
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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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<h3>Training the network<a class="headerlink" href="#training-the-network" title="Permalink to this headline">¶</a></h3>
|
||||
<ol class="arabic simple">
|
||||
<li>Sample a batch of transitions from the replay buffer.</li>
|
||||
<li>First, the next state quantiles are predicted. These are used in order to calculate the targets for the network,
|
||||
by following the Bellman equation.
|
||||
Next, the current quantile locations for the current states are predicted, sorted, and used for calculating the
|
||||
quantile midpoints targets.</li>
|
||||
<li>The network is trained with the quantile regression loss between the resulting quantile locations and the target
|
||||
quantile locations. Only the targets of the actions that were actually taken are updated.</li>
|
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<li>Once in every few thousand steps, weights are copied from the online network to the target network.</li>
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</ol>
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<dl class="class">
|
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<dt id="rl_coach.agents.qr_dqn_agent.QuantileRegressionDQNAlgorithmParameters">
|
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<em class="property">class </em><code class="descclassname">rl_coach.agents.qr_dqn_agent.</code><code class="descname">QuantileRegressionDQNAlgorithmParameters</code><a class="reference internal" href="../../../_modules/rl_coach/agents/qr_dqn_agent.html#QuantileRegressionDQNAlgorithmParameters"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#rl_coach.agents.qr_dqn_agent.QuantileRegressionDQNAlgorithmParameters" title="Permalink to this definition">¶</a></dt>
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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">
|
||||
<li><strong>atoms</strong> – (int)
|
||||
the number of atoms to predict for each action</li>
|
||||
<li><strong>huber_loss_interval</strong> – (float)
|
||||
One of the huber loss parameters, and is referred to as <span class="math notranslate nohighlight">\(\kapa\)</span> in the paper.
|
||||
It describes the interval [-k, k] in which the huber loss acts as a MSE loss.</li>
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<div class="section" id="rainbow">
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<h1>Rainbow<a class="headerlink" href="#rainbow" title="Permalink to this headline">¶</a></h1>
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<p><strong>Actions space:</strong> Discrete</p>
|
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<p><strong>References:</strong> <a class="reference external" href="https://arxiv.org/abs/1710.02298">Rainbow: Combining Improvements in 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/rainbow.png" class="align-center" src="../../../_images/rainbow.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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<p>Rainbow combines 6 recent advancements in reinforcement learning:</p>
|
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<ul class="simple">
|
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<li>N-step returns</li>
|
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<li>Distributional state-action value learning</li>
|
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<li>Dueling networks</li>
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<li>Noisy Networks</li>
|
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<li>Double DQN</li>
|
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<li>Prioritized Experience Replay</li>
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</ul>
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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>
|
||||
<ol class="arabic">
|
||||
<li><p class="first">Sample a batch of transitions from the replay buffer.</p>
|
||||
</li>
|
||||
<li><p class="first">The Bellman update is projected to the set of atoms representing the <span class="math notranslate nohighlight">\(Q\)</span> values distribution, such
|
||||
that the <span class="math notranslate nohighlight">\(i-th\)</span> component of the projected update is calculated as follows:</p>
|
||||
<p><span class="math notranslate nohighlight">\((\Phi \hat{T} Z_{\theta}(s_t,a_t))_i=\sum_{j=0}^{N-1}\Big[1-\frac{\lvert[\hat{T}_{z_{j}}]^{V_{MAX}}_{V_{MIN}}-z_i\rvert}{\Delta z}\Big]^1_0 \ p_j(s_{t+1}, \pi(s_{t+1}))\)</span></p>
|
||||
<p>where:
|
||||
* <span class="math notranslate nohighlight">\([ \cdot ]\)</span> bounds its argument in the range <span class="math notranslate nohighlight">\([a, b]\)</span>
|
||||
* <span class="math notranslate nohighlight">\(\hat{T}_{z_{j}}\)</span> is the Bellman update for atom
|
||||
<span class="math notranslate nohighlight">\(z_j\)</span>: <span class="math notranslate nohighlight">\(\hat{T}_{z_{j}} := r_t+\gamma r_{t+1} + ... + \gamma r_{t+n-1} + \gamma^{n-1} z_j\)</span></p>
|
||||
</li>
|
||||
<li><p class="first">Network is trained with the cross entropy loss between the resulting probability distribution and the target
|
||||
probability distribution. Only the target of the actions that were actually taken is updated.</p>
|
||||
</li>
|
||||
<li><p class="first">Once in every few thousand steps, weights are copied from the online network to the target network.</p>
|
||||
</li>
|
||||
<li><p class="first">After every training step, the priorities of the batch transitions are updated in the prioritized replay buffer
|
||||
using the KL divergence loss that is returned from the network.</p>
|
||||
</li>
|
||||
</ol>
|
||||
<dl class="class">
|
||||
<dt id="rl_coach.agents.rainbow_dqn_agent.RainbowDQNAlgorithmParameters">
|
||||
<em class="property">class </em><code class="descclassname">rl_coach.agents.rainbow_dqn_agent.</code><code class="descname">RainbowDQNAlgorithmParameters</code><a class="reference internal" href="../../../_modules/rl_coach/agents/rainbow_dqn_agent.html#RainbowDQNAlgorithmParameters"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#rl_coach.agents.rainbow_dqn_agent.RainbowDQNAlgorithmParameters" title="Permalink to this definition">¶</a></dt>
|
||||
<dd><table class="docutils field-list" frame="void" rules="none">
|
||||
<col class="field-name" />
|
||||
<col class="field-body" />
|
||||
<tbody valign="top">
|
||||
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
|
||||
<li><strong>n_step</strong> – (int)
|
||||
The number of steps to bootstrap the network over. The first N-1 steps actual rewards will be accumulated
|
||||
using an exponentially growing discount factor, and the Nth step will be bootstrapped from the network
|
||||
prediction.</li>
|
||||
<li><strong>store_transitions_only_when_episodes_are_terminated</strong> – (bool)
|
||||
If set to True, the transitions will be stored in an Episode object until the episode ends, and just then
|
||||
written to the memory. This is useful since we want to calculate the N-step discounted rewards before saving the
|
||||
transitions into the memory, and to do so we need the entire episode first.</li>
|
||||
</ul>
|
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</td>
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</tr>
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</tbody>
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