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<div class="section" id="control-flow">
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<h1>Control Flow<a class="headerlink" href="#control-flow" title="Permalink to this headline">¶</a></h1>
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<p>Coach is built in a modular way, encouraging modules reuse and reducing the amount of boilerplate code needed
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for developing new algorithms or integrating a new challenge as an environment.
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On the other hand, it can be overwhelming for new users to ramp up on the code.
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To help with that, here’s a short overview of the control flow.</p>
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<div class="section" id="graph-manager">
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<h2>Graph Manager<a class="headerlink" href="#graph-manager" title="Permalink to this headline">¶</a></h2>
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<p>The main entry point for Coach is <code class="code docutils literal notranslate"><span class="pre">coach.py</span></code>.
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The main functionality of this script is to parse the command line arguments and invoke all the sub-processes needed
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for the given experiment.
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<code class="code docutils literal notranslate"><span class="pre">coach.py</span></code> executes the given <strong>preset</strong> file which returns a <code class="code docutils literal notranslate"><span class="pre">GraphManager</span></code> object.</p>
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<p>A <strong>preset</strong> is a design pattern that is intended for concentrating the entire definition of an experiment in a single
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file. This helps with experiments reproducibility, improves readability and prevents confusion.
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The outcome of a preset is a <code class="code docutils literal notranslate"><span class="pre">GraphManager</span></code> which will usually be instantiated in the final lines of the preset.</p>
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<p>A <code class="code docutils literal notranslate"><span class="pre">GraphManager</span></code> is an object that holds all the agents and environments of an experiment, and is mostly responsible
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for scheduling their work. Why is it called a <strong>graph</strong> manager? Because agents and environments are structured into
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a graph of interactions. For example, in hierarchical reinforcement learning schemes, there will often be a master
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policy agent, that will control a sub-policy agent, which will interact with the environment. Other schemes can have
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much more complex graphs of control, such as several hierarchy layers, each with multiple agents.
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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><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.</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.</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.</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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<h2>Level Manager<a class="headerlink" href="#level-manager" title="Permalink to this headline">¶</a></h2>
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<p>In each of the 3 phases described above, the graph manager will invoke all the hierarchy levels in the graph in a
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synchronized manner. In Coach, agents do not interact directly with the environment. Instead, they go through a
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<em>LevelManager</em>, which is a proxy that manages their interaction. The level manager passes the current state and reward
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from the environment to the agent, and the actions from the agent to the environment.</p>
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<p>The motivation for having a level manager is to disentangle the code of the environment and the agent, so to allow more
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complex interactions. Each level can have multiple agents which interact with the environment. Who gets to choose the
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action for each step is controlled by the level manager.
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Additionally, each level manager can act as an environment for the hierarchy level above it, such that each hierarchy
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level can be seen as an interaction between an agent and an environment, even if the environment is just more agents in
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a lower hierarchy level.</p>
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</div>
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<div class="section" id="agent">
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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><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.</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><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.</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><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.</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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