Practical Risk Measures in Reinforcement Learning

Dotan Di Castro; Joel Oren; Shie Mannor

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arXiv · 1908.08379

Practical Risk Measures in Reinforcement Learning

Dotan Di Castro·Joel Oren·Shie Mannor

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Abstract

Practical application of Reinforcement Learning (RL) often involves risk considerations. We study a generalized approximation scheme for risk measures, based on Monte-Carlo simulations, where the risk measures need not necessarily be \emph{coherent}. We demonstrate that, even in simple problems, measures such as the variance of the reward-to-go do not capture the risk in a satisfactory manner. In addition, we show how a risk measure can be derived from model's realizations. We propose a neural architecture for estimating the risk and suggest the risk critic architecture that can be use to optimize a policy under general risk measures. We conclude our work with experiments that demonstrate the efficacy of our approach.

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Dotan Di Castro, Joel Oren, Shie Mannor. 2019-08-22. Practical Risk Measures in Reinforcement Learning. https://arxiv.org/abs/1908.08379

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