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RAPTOR: End-to-end Risk-Aware MDP Planning and Policy Learning by Backpropagation
[article]
2021
arXiv
pre-print
Planning provides a framework for optimizing sequential decisions in complex environments. Recent advances in efficient planning in deterministic or stochastic high-dimensional domains with continuous action spaces leverage backpropagation through a model of the environment to directly optimize actions. However, existing methods typically not take risk into account when optimizing in stochastic domains, which can be incorporated efficiently in MDPs by optimizing the entropic utility of returns.
arXiv:2106.07260v1
fatcat:3fg4fwrzevhp7ayizd4etdfp5a