Epistemic risk-sensitive reinforcement learning

Abstract

We develop a framework for risk-sensitive behaviour in reinforcement learning (RL) due to uncertainty about the environment dynamics by leveraging utility-based definitions of risk sensitivity. In this framework, the preference for risk can be tuned by varying the utility function, for which we develop dynamic programming (DP) and policy gradient-based algorithms. The risk-averse behavior is compared with the behavior of risk-neutral policy in environments with epistemic risk.