Entropic Risk Constrained Soft-Robust Policy Optimization

Reazul Hasan Russel; Bahram Behzadian; Marek Petrik

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arXiv · 2006.11679

Entropic Risk Constrained Soft-Robust Policy Optimization

Reazul Hasan Russel·Bahram Behzadian·Marek Petrik

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Abstract

Having a perfect model to compute the optimal policy is often infeasible in reinforcement learning. It is important in high-stakes domains to quantify and manage risk induced by model uncertainties. Entropic risk measure is an exponential utility-based convex risk measure that satisfies many reasonable properties. In this paper, we propose an entropic risk constrained policy gradient and actor-critic algorithms that are risk-averse to the model uncertainty. We demonstrate the usefulness of our algorithms on several problem domains.

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Reazul Hasan Russel, Bahram Behzadian, Marek Petrik. 2020-06-20. Entropic Risk Constrained Soft-Robust Policy Optimization. https://arxiv.org/abs/2006.11679

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