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arXiv · 2209.04067

RASR: Risk-Averse Soft-Robust MDPs with EVaR and Entropic Risk

Jia Lin Hau·Marek Petrik·Mohammad Ghavamzadeh·Reazul Russel
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Abstract

Prior work on safe Reinforcement Learning (RL) has studied risk-aversion to randomness in dynamics (aleatory) and to model uncertainty (epistemic) in isolation. We propose and analyze a new framework to jointly model the risk associated with epistemic and aleatory uncertainties in finite-horizon and discounted infinite-horizon MDPs. We call this framework that combines Risk-Averse and Soft-Robust methods RASR. We show that when the risk-aversion is defined using either EVaR or the entropic risk, the optimal policy in RASR can be computed efficiently using a new dynamic program formulation with a time-dependent risk level. As a result, the optimal risk-averse policies are deterministic but time-dependent, even in the infinite-horizon discounted setting. We also show that particular RASR objectives reduce to risk-averse RL with mean posterior transition probabilities. Our empirical results show that our new algorithms consistently mitigate uncertainty as measured by EVaR and other standard risk measures.

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Jia Lin Hau, Marek Petrik, Mohammad Ghavamzadeh, Reazul Russel. 2022-09-14. RASR: Risk-Averse Soft-Robust MDPs with EVaR and Entropic Risk. https://arxiv.org/abs/2209.04067

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