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* updating the documentation website * adding the built docs * update of api docstrings across coach and tutorials 0-2 * added some missing api documentation * New Sphinx based documentation
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22 lines
1.3 KiB
ReStructuredText
Benchmarks
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==========
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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:
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* 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.
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* 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 **a lot** of
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hyper-parameters to set.
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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.
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To see the benchmark results, please visit the
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`following GitHub page <https://github.com/NervanaSystems/coach/tree/master/benchmarks>`_. |