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* Currently this is specific to the case of discretizing a continuous action space. Can easily be adapted to other case by feeding the kNN otherwise, and removing the usage of a discretizing output action filter
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<div class="section" id="benchmarks">
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<h1>Benchmarks<a class="headerlink" href="#benchmarks" title="Permalink to this headline">¶</a></h1>
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<p>Reinforcement learning is a developing field, and so far it has been particularly difficult to reproduce some of the
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results published in the original papers. Some reasons for this are:</p>
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<ul class="simple">
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<li><p>Reinforcement learning algorithms are notoriously known as having an unstable learning process.
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The data the neural networks trains on is dynamic, and depends on the random seed defined for the environment.</p></li>
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<li><p>Reinforcement learning algorithms have many moving parts. For some environments and agents, there are many
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“tricks” which are needed to get the exact behavior the paper authors had seen. Also, there are <strong>a lot</strong> of
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hyper-parameters to set.</p></li>
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</ul>
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<p>In order for a reinforcement learning implementation to be useful for research or for data science, it must be
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shown that it achieves the expected behavior. For this reason, we collected a set of benchmark results from most
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of the algorithms implemented in Coach. The algorithms were tested on a subset of the same environments that were
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used in the original papers, and with multiple seed for each environment.
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Additionally, Coach uses some strict testing mechanisms to try and make sure the results we show for these
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benchmarks stay intact as Coach continues to develop.</p>
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<p>To see the benchmark results, please visit the
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<a class="reference external" href="https://github.com/NervanaSystems/coach/tree/master/benchmarks">following GitHub page</a>.</p>
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