핵심 개념
RS-DisRL introduces efficient algorithms for risk-sensitive reinforcement learning with static Lipschitz risk measures.
초록
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.
- Introduction to Risk-Sensitive RL
- Challenges in Sample Complexity
- Model-Based and Model-Free Approaches
- Theoretical Guarantees for RS-DisRL
- Value Function Approximation
통계
RS-DisRL-M 알고리즘은 e√K의 regret 상한을 달성합니다.
인용구
"RS-DisRL-M은 첫 번째 통계적으로 효율적인 RSRL 알고리즘을 제공합니다."