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Poster

Risk-Sensitive Policy Optimization via Predictive CVaR Policy Gradient

Ju-Hyun Kim · Seungki Min

Hall C 4-9 #1503
[ ]
Tue 23 Jul 2:30 a.m. PDT — 4 a.m. PDT

Abstract:

This paper addresses a policy optimization task with the conditional value-at-risk (CVaR) objective. We introduce the predictive CVaR policy gradient, a novel approach that seamlessly integrates risk-neutral policy gradient algorithms with minimal modifications. Our method incorporates a reweighting strategy in gradient calculation -- individual cost terms are reweighted in proportion to their predicted contribution to the objective. These weights can be easily estimated through a separate learning procedure. We provide theoretical and empirical analyses, demonstrating the validity and effectiveness of our proposed method.

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