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Enabling Coach Documentation to be run even when environments are not installed (#326)
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anabwan
@@ -8,7 +8,7 @@
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<title>Control Flow — Reinforcement Learning Coach 0.11.0 documentation</title>
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<title>Control Flow — Reinforcement Learning Coach 0.12.1 documentation</title>
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<link rel="stylesheet" href="../_static/css/custom.css" type="text/css" />
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@@ -33,21 +41,16 @@
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<link rel="prev" title="Coach Dashboard" href="../dashboard.html" />
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<link href="../_static/css/custom.css" rel="stylesheet" type="text/css">
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@@ -210,17 +213,17 @@ The graph manager’s main loop is the improve loop.</p>
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<a class="reference internal image-reference" href="../_images/improve.png"><img alt="../_images/improve.png" class="align-center" src="../_images/improve.png" style="width: 400px;" /></a>
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<p>The improve loop skips between 3 main phases - heatup, training and evaluation:</p>
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<ul class="simple">
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<li><strong>Heatup</strong> - the goal of this phase is to collect initial data for populating the replay buffers. The heatup phase
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<li><p><strong>Heatup</strong> - the goal of this phase is to collect initial data for populating the replay buffers. The heatup phase
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takes place only in the beginning of the experiment, and the agents will act completely randomly during this phase.
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Importantly, the agents do not train their networks during this phase. DQN for example, uses 50k random steps in order
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to initialize the replay buffers.</li>
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<li><strong>Training</strong> - the training phase is the main phase of the experiment. This phase can change between agent types,
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to initialize the replay buffers.</p></li>
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<li><p><strong>Training</strong> - the training phase is the main phase of the experiment. This phase can change between agent types,
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but essentially consists of repeated cycles of acting, collecting data from the environment, and training the agent
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networks. During this phase, the agent will use its exploration policy in training mode, which will add noise to its
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actions in order to improve its knowledge about the environment state space.</li>
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<li><strong>Evaluation</strong> - the evaluation phase is intended for evaluating the current performance of the agent. The agents
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actions in order to improve its knowledge about the environment state space.</p></li>
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<li><p><strong>Evaluation</strong> - the evaluation phase is intended for evaluating the current performance of the agent. The agents
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will act greedily in order to exploit the knowledge aggregated so far and the performance over multiple episodes of
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evaluation will be averaged in order to reduce the stochasticity effects of all the components.</li>
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evaluation will be averaged in order to reduce the stochasticity effects of all the components.</p></li>
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</ul>
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</div>
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<div class="section" id="level-manager">
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@@ -240,29 +243,29 @@ a lower hierarchy level.</p>
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<h2>Agent<a class="headerlink" href="#agent" title="Permalink to this headline">¶</a></h2>
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<p>The base agent class has 3 main function that will be used during those phases - observe, act and train.</p>
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<ul class="simple">
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<li><strong>Observe</strong> - this function gets the latest response from the environment as input, and updates the internal state
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<li><p><strong>Observe</strong> - this function gets the latest response from the environment as input, and updates the internal state
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of the agent with the new information. The environment response will
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be first passed through the agent’s <code class="code docutils literal notranslate"><span class="pre">InputFilter</span></code> object, which will process the values in the response, according
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to the specific agent definition. The environment response will then be converted into a
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<code class="code docutils literal notranslate"><span class="pre">Transition</span></code> which will contain the information from a single step
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<span class="math notranslate nohighlight">\((s_{t}, a_{t}, r_{t}, s_{t+1}, \textrm{terminal signal})\)</span>, and store it in the memory.</li>
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<span class="math notranslate nohighlight">\((s_{t}, a_{t}, r_{t}, s_{t+1}, \textrm{terminal signal})\)</span>, and store it in the memory.</p></li>
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</ul>
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<a class="reference internal image-reference" href="../_images/observe.png"><img alt="../_images/observe.png" class="align-center" src="../_images/observe.png" style="width: 700px;" /></a>
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<ul class="simple">
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<li><strong>Act</strong> - this function uses the current internal state of the agent in order to select the next action to take on
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<li><p><strong>Act</strong> - this function uses the current internal state of the agent in order to select the next action to take on
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the environment. This function will call the per-agent custom function <code class="code docutils literal notranslate"><span class="pre">choose_action</span></code> that will use the network
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and the exploration policy in order to select an action. The action will be stored, together with any additional
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information (like the action value for example) in an <code class="code docutils literal notranslate"><span class="pre">ActionInfo</span></code> object. The ActionInfo object will then be
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passed through the agent’s <code class="code docutils literal notranslate"><span class="pre">OutputFilter</span></code> to allow any processing of the action (like discretization,
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or shifting, for example), before passing it to the environment.</li>
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or shifting, for example), before passing it to the environment.</p></li>
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</ul>
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<a class="reference internal image-reference" href="../_images/act.png"><img alt="../_images/act.png" class="align-center" src="../_images/act.png" style="width: 700px;" /></a>
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<ul class="simple">
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<li><strong>Train</strong> - this function will sample a batch from the memory and train on it. The batch of transitions will be
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<li><p><strong>Train</strong> - this function will sample a batch from the memory and train on it. The batch of transitions will be
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first wrapped into a <code class="code docutils literal notranslate"><span class="pre">Batch</span></code> object to allow efficient querying of the batch values. It will then be passed into
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the agent specific <code class="code docutils literal notranslate"><span class="pre">learn_from_batch</span></code> function, that will extract network target values from the batch and will
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train the networks accordingly. Lastly, if there’s a target network defined for the agent, it will sync the target
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network weights with the online network.</li>
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network weights with the online network.</p></li>
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</ul>
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<a class="reference internal image-reference" href="../_images/train.png"><img alt="../_images/train.png" class="align-center" src="../_images/train.png" style="width: 700px;" /></a>
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</div>
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@@ -279,7 +282,7 @@ network weights with the online network.</li>
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<a href="network.html" class="btn btn-neutral float-right" title="Network Design" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right"></span></a>
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<a href="../dashboard.html" class="btn btn-neutral" title="Coach Dashboard" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left"></span> Previous</a>
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<a href="../dashboard.html" class="btn btn-neutral float-left" title="Coach Dashboard" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left"></span> Previous</a>
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</div>
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@@ -288,7 +291,7 @@ network weights with the online network.</li>
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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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@@ -305,27 +308,16 @@ network weights with the online network.</li>
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<title>Distributed Coach - Horizontal Scale-Out — Reinforcement Learning Coach 0.11.0 documentation</title>
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<title>Distributed Coach - Horizontal Scale-Out — Reinforcement Learning Coach 0.12.1 documentation</title>
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<script type="text/javascript" id="documentation_options" data-url_root="../" src="../_static/documentation_options.js"></script>
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<link rel="prev" title="Network Design" href="network.html" />
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<link href="../_static/css/custom.css" rel="stylesheet" type="text/css">
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three interfaces for horizontal scale-out, which allows for integration with different technologies and flexibility.
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These three interfaces are orchestrator, memory backend and data store.</p>
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<ul class="simple">
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<li><strong>Orchestrator</strong> - The orchestrator interface provides basic interaction points for orchestration, scheduling and
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<li><p><strong>Orchestrator</strong> - The orchestrator interface provides basic interaction points for orchestration, scheduling and
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resource management of training and rollout workers in the distributed coach mode. The interactions points define
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how Coach should deploy, undeploy and monitor the workers spawned by Coach.</li>
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<li><strong>Memory Backend</strong> - This interface is used as the backing store or stream for the memory abstraction in
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how Coach should deploy, undeploy and monitor the workers spawned by Coach.</p></li>
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<li><p><strong>Memory Backend</strong> - This interface is used as the backing store or stream for the memory abstraction in
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distributed Coach. The implementation of this module is mainly used for communicating experiences (transitions
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and episodes) from the rollout to the training worker.</li>
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<li><strong>Data Store</strong> - This interface is used as a backing store for the policy checkpoints. It is mainly used to
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synchronizing policy checkpoints from the training to the rollout worker.</li>
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and episodes) from the rollout to the training worker.</p></li>
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<li><p><strong>Data Store</strong> - This interface is used as a backing store for the policy checkpoints. It is mainly used to
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synchronizing policy checkpoints from the training to the rollout worker.</p></li>
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</ul>
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<a class="reference internal image-reference" href="../_images/horizontal-scale-out.png"><img alt="../_images/horizontal-scale-out.png" class="align-center" src="../_images/horizontal-scale-out.png" style="width: 800px;" /></a>
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<div class="section" id="supported-synchronization-types">
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@@ -207,12 +210,12 @@ rollout worker. For each algorithm, it is specified by using the <cite>Distribut
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<cite>agent_params.algorithm.distributed_coach_synchronization_type</cite> in the preset. In distributed Coach, two types of
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synchronization modes are supported: <cite>SYNC</cite> and <cite>ASYNC</cite>.</p>
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<ul class="simple">
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<li><strong>SYNC</strong> - In this type, the trainer waits for all the experiences to be gathered from distributed rollout workers
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<li><p><strong>SYNC</strong> - In this type, the trainer waits for all the experiences to be gathered from distributed rollout workers
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before training a new policy and the rollout workers wait for a new policy before gathering experiences. It is suitable
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for ON policy algorithms.</li>
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<li><strong>ASYNC</strong> - In this type, the trainer doesn’t wait for any set of experiences to be gathered from distributed
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for ON policy algorithms.</p></li>
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<li><p><strong>ASYNC</strong> - In this type, the trainer doesn’t wait for any set of experiences to be gathered from distributed
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rollout workers and the rollout workers continously gather experiences loading new policies, whenever they become
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available. It is suitable for OFF policy algorithms.</li>
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available. It is suitable for OFF policy algorithms.</p></li>
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</ul>
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</div>
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</div>
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@@ -228,7 +231,7 @@ available. It is suitable for OFF policy algorithms.</li>
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<a href="../contributing/add_agent.html" class="btn btn-neutral float-right" title="Adding a New Agent" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right"></span></a>
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<a href="network.html" class="btn btn-neutral" title="Network Design" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left"></span> Previous</a>
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<a href="network.html" class="btn btn-neutral float-left" title="Network Design" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left"></span> Previous</a>
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@@ -237,7 +240,7 @@ available. It is suitable for OFF policy algorithms.</li>
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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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@@ -254,27 +257,16 @@ available. It is suitable for OFF policy algorithms.</li>
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<title>Network Design — Reinforcement Learning Coach 0.11.0 documentation</title>
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<title>Network Design — Reinforcement Learning Coach 0.12.1 documentation</title>
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<link rel="prev" title="Control Flow" href="control_flow.html" />
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<link href="../_static/css/custom.css" rel="stylesheet" type="text/css">
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@@ -190,22 +193,21 @@
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The network is designed in a modular way to allow reusability in different agents.
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It is separated into three main parts:</p>
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<ul>
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<li><p class="first"><strong>Input Embedders</strong> - This is the first stage of the network, meant to convert the input into a feature vector representation.
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<li><p><strong>Input Embedders</strong> - This is the first stage of the network, meant to convert the input into a feature vector representation.
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It is possible to combine several instances of any of the supported embedders, in order to allow varied combinations of inputs.</p>
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<blockquote>
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<div><p>There are two main types of input embedders:</p>
|
||||
<ol class="arabic simple">
|
||||
<li>Image embedder - Convolutional neural network.</li>
|
||||
<li>Vector embedder - Multi-layer perceptron.</li>
|
||||
<li><p>Image embedder - Convolutional neural network.</p></li>
|
||||
<li><p>Vector embedder - Multi-layer perceptron.</p></li>
|
||||
</ol>
|
||||
</div></blockquote>
|
||||
</li>
|
||||
<li><p class="first"><strong>Middlewares</strong> - The middleware gets the output of the input embedder, and processes it into a different representation domain,
|
||||
<li><p><strong>Middlewares</strong> - The middleware gets the output of the input embedder, and processes it into a different representation domain,
|
||||
before sending it through the output head. The goal of the middleware is to enable processing the combined outputs of
|
||||
several input embedders, and pass them through some extra processing.
|
||||
This, for instance, might include an LSTM or just a plain simple FC layer.</p>
|
||||
</li>
|
||||
<li><p class="first"><strong>Output Heads</strong> - The output head is used in order to predict the values required from the network.
|
||||
This, for instance, might include an LSTM or just a plain simple FC layer.</p></li>
|
||||
<li><p><strong>Output Heads</strong> - The output head is used in order to predict the values required from the network.
|
||||
These might include action-values, state-values or a policy. As with the input embedders,
|
||||
it is possible to use several output heads in the same network. For example, the <em>Actor Critic</em> agent combines two
|
||||
heads - a policy head and a state-value head.
|
||||
@@ -222,12 +224,12 @@ and are often synchronized either locally or between parallel workers. For easie
|
||||
a wrapper around these copies exposes a simplified API, which allows hiding these complexities from the agent.
|
||||
In this wrapper, 3 types of networks can be defined:</p>
|
||||
<ul class="simple">
|
||||
<li><strong>online network</strong> - A mandatory network which is the main network the agent will use</li>
|
||||
<li><strong>global network</strong> - An optional network which is shared between workers in single-node multi-process distributed learning.
|
||||
It is updated by all the workers directly, and holds the most up-to-date weights.</li>
|
||||
<li><strong>target network</strong> - An optional network which is local for each worker. It can be used in order to keep a copy of
|
||||
<li><p><strong>online network</strong> - A mandatory network which is the main network the agent will use</p></li>
|
||||
<li><p><strong>global network</strong> - An optional network which is shared between workers in single-node multi-process distributed learning.
|
||||
It is updated by all the workers directly, and holds the most up-to-date weights.</p></li>
|
||||
<li><p><strong>target network</strong> - An optional network which is local for each worker. It can be used in order to keep a copy of
|
||||
the weights stable for a long period of time. This is used in different agents, like DQN for example, in order to
|
||||
have stable targets for the online network while training it.</li>
|
||||
have stable targets for the online network while training it.</p></li>
|
||||
</ul>
|
||||
<a class="reference internal image-reference" href="../_images/distributed.png"><img alt="../_images/distributed.png" class="align-center" src="../_images/distributed.png" style="width: 600px;" /></a>
|
||||
</div>
|
||||
@@ -244,7 +246,7 @@ have stable targets for the online network while training it.</li>
|
||||
<a href="horizontal_scaling.html" class="btn btn-neutral float-right" title="Distributed Coach - Horizontal Scale-Out" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right"></span></a>
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<a href="control_flow.html" class="btn btn-neutral" title="Control Flow" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left"></span> Previous</a>
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<a href="control_flow.html" class="btn btn-neutral float-left" title="Control Flow" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left"></span> Previous</a>
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||||
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||||
</div>
|
||||
|
||||
@@ -253,7 +255,7 @@ have stable targets for the online network while training it.</li>
|
||||
|
||||
<div role="contentinfo">
|
||||
<p>
|
||||
© Copyright 2018, Intel AI Lab
|
||||
© Copyright 2018-2019, Intel AI Lab
|
||||
|
||||
</p>
|
||||
</div>
|
||||
@@ -270,27 +272,16 @@ have stable targets for the online network while training it.</li>
|
||||
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||||
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Reference in New Issue
Block a user