Risk-Constrained Reinforcement Learning with Percentile Risk Criteria

Yinlam Chow, Mohammad Ghavamzadeh, Lucas Janson, Marco Pavone; 18(167):1−51, 2018.

Abstract

In many sequential decision-making problems one is interested in minimizing an expected cumulative cost while taking into account \emph{risk}, i.e., increased awareness of events of small probability and high consequences. Accordingly, the objective of this paper is to present efficient reinforcement learning algorithms for risk-constrained Markov decision processes (MDPs), where risk is represented via a {\color{black} chance constraint} or a constraint on the conditional value-at-risk (CVaR) of the cumulative cost. We collectively refer to such problems as percentile risk-constrained MDPs. Specifically, we first derive a formula for computing the gradient of the Lagrangian function for percentile risk-constrained MDPs. Then, we devise policy gradient and actor-critic algorithms that (1) estimate such gradient, (2) update the policy in the descent direction, and (3) update the Lagrange multiplier in the ascent direction. For these algorithms we prove convergence to {\color{black} locally optimal} policies. Finally, we demonstrate the effectiveness of our algorithms in an optimal stopping problem and an online marketing application.

[abs][pdf][bib]




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