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<div class="section" id="batch-reinforcement-learning">
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<h1>Batch Reinforcement Learning<a class="headerlink" href="#batch-reinforcement-learning" title="Permalink to this headline">¶</a></h1>
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<p>Coach supports Batch Reinforcement Learning, where learning is based solely on a (fixed) batch of data.
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In Batch RL, we are given a dataset of experience, which was collected using some (one or more) deployed policies, and we would
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like to use it to learn a better policy than what was used to collect the dataset.
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There is no simulator to interact with, and so we cannot collect any new data, meaning we often cannot explore the MDP any further.
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To make things even harder, we would also like to use the dataset in order to evaluate the newly learned policy
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(using off-policy evaluation), since we do not have a simulator which we can use to evaluate the policy on.
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Batch RL is also often beneficial in cases where we just want to separate the inference (data collection) from the
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training process of a new policy. This is often the case where we have a system on which we could quite easily deploy a policy
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and collect experience data, but cannot easily use that system’s setup to online train a new policy (as is often the
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case with more standard RL algorithms).</p>
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<p>Coach supports (almost) all of the integrated off-policy algorithms with Batch RL.</p>
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<p>A lot more details and example usage can be found in the
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<a class="reference external" href="https://github.com/NervanaSystems/coach/blob/master/tutorials/4.%20Batch%20Reinforcement%20Learning.ipynb">tutorial</a>.</p>
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