This inefficiency stems from two main facts: a focus on tail-end performance that overlooks many sampled trajectories, and the potential of gradient vanishing when the lower tail of the return distribution is overly flat. To address these challenges, we propose a simple mixture policy parameterization. This method integrates a risk-neutral policy with an adjustable policy to form a risk-averse policy. By employing this strategy, all collected trajectories can be utilized for policy updating, and the issue of vanishing gradients is counteracted by stimulating higher returns through the risk-neutral component, thus lifting the tail and preventing flatness. Our empirical study reveals that this mixture parameterization is uniquely effective across a variety of benchmark domains. Specifically, it excels in identifying risk-averse CVaR policies in some Mujoco environments where the traditional CVaR-PG fails to learn a reasonable policy.

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.