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Provable Risk-Sensitive Distributional Reinforcement Learning with General Function Approximation
Feb. 29, 2024, 5:41 a.m. | Yu Chen, Xiangcheng Zhang, Siwei Wang, Longbo Huang
cs.LG updates on arXiv.org arxiv.org
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
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