Efficient Risk-Averse Reinforcement Learning. (arXiv:2205.05138v1 [cs.LG])

Web: http://arxiv.org/abs/2205.05138

In risk-averse reinforcement learning (RL), the goal is to optimize some risk
measure of the returns. A risk measure often focuses on the worst returns out
of the agent's experience. As a result, standard methods for risk-averse RL
often ignore high-return strategies. We prove that under certain conditions
this inevitably leads to a local-optimum barrier, and propose a soft risk
mechanism to bypass it. We also devise a novel Cross Entropy module for risk
sampling, which (1) preserves risk aversion …

arxiv learning reinforcement reinforcement learning risk