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Hindsight Credit Assignment
[article]
2019
arXiv
pre-print
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
arXiv:1912.02503v1
fatcat:jaufpb2dobgl5igp7exzm5u2su