Provable Risk-Sensitive Distributional Reinforcement Learning with General Function Approximation

arXiv:2402.18159v1 Announce Type: new
Abstract: In the realm of reinforcement learning (RL), accounting for risk is crucial for making decisions under uncertainty, particularly in applications where safety and reliability are paramount. In this paper, we introduce a general framework on Risk-Sensitive Distributional Reinforcement Learning (RS-DisRL), with static Lipschitz Risk Measures (LRM) and general function approximation. Our framework covers a broad class of risk-sensitive RL, and facilitates analysis of the impact of estimation functions on the effectiveness of RSRL strategies and …

abstract accounting applications approximation arxiv cs.lg decisions framework function general lrm making paper reinforcement reinforcement learning reliability risk safety type uncertainty