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<li class="toctree-l3"><a href="#what-is-coach">What is Coach?</a></li>
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<li><a class="toctree-l4" href="#motivation">Motivation</a></li>
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<li><a class="toctree-l4" href="#solution">Solution</a></li>
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<a class="" href="algorithms/value_optimization/dqn/index.html">DQN</a>
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<a class="" href="algorithms/policy_optimization/ac/index.html">Actor-Critic</a>
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<a class="" href="algorithms/imitation/bc/index.html">Behavioral Cloning</a>
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<a class="" href="dashboard/index.html">Coach Dashboard</a>
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<a class="" href="contributing/add_agent/index.html">Adding a New Agent</a>
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<h1 id="what-is-coach">What is Coach?</h1>
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<h2 id="motivation">Motivation</h2>
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<p>Train and evaluate reinforcement learning agents by harnessing the power of multi-core CPU processing to achieve state-of-the-art results. Provide a sandbox for easing the development process of new algorithms through a modular design and an elegant set of APIs. </p>
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<h2 id="solution">Solution</h2>
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<p>Coach is a python environment which models the interaction between an agent and an environment in a modular way.
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With Coach, it is possible to model an agent by combining various building blocks, and training the agent on multiple environments.
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The available environments allow testing the agent in different practical fields such as robotics, autonomous driving, games and more.
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Coach collects statistics from the training process and supports advanced visualization techniques for debugging the agent being trained.</p>
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<p>Blog post from the Intel® Nervana™ website can be found <a href="https://www.intelnervana.com/reinforcement-learning-coach-intel">here</a>. </p>
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<p>GitHub repository is <a href="https://github.com/NervanaSystems/coach">here</a>. </p>
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<h2 id="design">Design</h2>
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<p><img src="img/design.png" alt="Coach Design" style="width: 800px;"/></p>
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