Reward Is Not Enough for Risk-Averse Reinforcement Learning

Why can’t we address risk-sensitivity just by setting the rewards properly?

TL;DR: Risk-aversion is essential in many RL applications (e.g., driving, robotic surgery and finance). Some modified RL frameworks consider risk (e.g., by optimizing a risk-measure of the return instead of its expectation), but pose new algorithmic challenges. Instead, it is often suggested to stick with the old and good RL framework, and just set the rewards such that negative outcomes are amplified. Unfortunately, as discussed below, modeling risk …

artificial intelligence machine learning reinforcement reinforcement learning risk risk-management