Entropic Risk Constrained Soft-Robust Policy Optimization

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.

References in corpus (3)

• Algorithms for CVaR Optimization in MDPs
• Safe Policy Improvement by Minimizing Robust Baseline Regret
• Policy Gradient for Coherent Risk Measures