Efficient Risk-Averse Reinforcement Learning

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.