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 mechanism we call soft risk to bypass it. We also devise a novel cross entropy module for sampling, which (1) preserves risk aversion despite the soft risk; (2) independently improves sample efficiency.

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 mechanism we call soft risk to bypass it. We also devise a novel cross entropy module for sampling, which (1) preserves risk aversion despite the soft risk; (2) independently improves sample efficiency.

In this post I present our recent NeurIPS 2022 paper (co-authored with Yinlam Chow, Mohammad Ghavamzadeh and Shie Mannor) about risk-averse reinforcement learning (RL). I discuss why and how risk aversion is applied to RL, what its limitations are, and how we propose to overcome them. An application to accidents prevention in autonomous driving is demonstrated. Our code is also available on GitHub. Risk-averse RL is crucial when applying RL to risk-sensitive real-world problems.