The paper introduces RS-DisRL for risk-sensitive RL with static LRM and general function approximation. It covers model-based and model-free approaches, providing theoretical guarantees for efficient learning. The work addresses challenges in sample complexity and extends to value function approximation.
Vers une autre langue
à partir du contenu source
arxiv.org
Questions plus approfondies