A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit <a rel="external noopener" href="https://arxiv.org/pdf/1912.02503v1.pdf">the original URL</a>. The file type is <code>application/pdf</code>.
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We consider the problem of efficient credit assignment in reinforcement learning. In order to efficiently and meaningfully utilize new data, we propose to explicitly assign credit to past decisions based on the likelihood of them having led to the observed outcome. This approach uses new information in hindsight, rather than employing foresight. Somewhat surprisingly, we show that value functions can be rewritten through this lens, yielding a new family of algorithms. We study the properties of<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1912.02503v1">arXiv:1912.02503v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/jaufpb2dobgl5igp7exzm5u2su">fatcat:jaufpb2dobgl5igp7exzm5u2su</a> </span>
more »... these algorithms, and empirically show that they successfully address important credit assignment challenges, through a set of illustrative tasks.
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