Provably Efficient Risk-Sensitive Offline Reinforcement Learning via Optimized Certainty Equivalents
arXiv:2610.21870v1 Announce Type: cross Abstract: Risk-sensitive reinforcement learning (RL) addresses decision-making under uncertainty and adverse outcomes. Learning risk-sensitive policies from fixed datasets is important when additional interaction with the environment is costly. Existing work on risk-sensitive RL focuses on online settings or specific risk measures, leaving a general theoretical understanding of its offline setting underdeveloped. In this work, we investigate efficient off…