Breakthrough in RL: Near-Optimal Sample Complexity for Recursive Entropic Risk Reinforcement Learning

Summary:A new study published on arXiv presents significant advancements in the sample complexity of reinforcement learning (RL) under recursive entropic risk preferences. The research focuses on finite-horizon discounted Markov decision processes (MDPs) with access to a generative model and introduces refined analysis for the model-based risk-sensitive Q-value iteration (MB-RS-QVI) algorithm. The study achieves near-optimal sample complexity bounds, outperforming existing guarantees in terms of the eff

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Background and Motivation

Reinforcement Learning (RL) is widely applied in handling complex decision-making tasks, but traditional RL algorithms face challenges when dealing with risk-sensitive tasks. Recursive entropic risk preferences offer a way to quantify risk, but the sample complexity issues in RL under this framework have not been fully addressed.

Key Contributions

1. Improved MB-RS-QVI Algorithm: The study introduces an enhanced model-based risk-sensitive Q-value iteration (MB-RS-QVI) algorithm that optimizes the sample complexity of learning the optimal Q-value function and an ε-optimal policy, given access to a generative model.
2. Theoretical Breakthrough: The research surpasses existing guarantees in terms of the effective horizon dependence and matches theoretical lower bounds in key parameters for the first time, eliminating the exponential gap between known upper and lower bounds.
3. Experimental Validation: Theoretical analysis and experimental results demonstrate the effectiveness of the method in handling risk-sensitive tasks.

Technical Highlights

• Sample Complexity Optimization for Risk-Sensitive RL: The study achieves sample complexity optimization under recursive entropic risk preferences, filling a gap in existing theory.
• Elimination of Exponential Gap: The improved algorithm eliminates the exponential gap between upper and lower bounds, leaving only a polynomial gap in the effective horizon.
• Application of Generative Models: The results demonstrate stronger theoretical guarantees and practical application potential when using generative models.

Industry Impact and Developer Recommendations

This research provides a more efficient theoretical foundation for risk-sensitive RL, which is significant for fields such as finance, autonomous driving, and robotics. Developers can refer to the study's findings to optimize existing RL algorithms for risk-sensitive tasks. Additionally, the theoretical framework and experimental methods proposed in the study offer new directions for future research.

Future Directions

Future research could further explore sample complexity optimization under different risk preferences and conduct broader validation in practical application scenarios. Moreover, applying the research results to larger-scale RL systems is a promising area for further investigation.

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Source: ArXiv Machine Learning (cs.LG) (2026-10-07)

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Tags: #Reinforcement Learning Follow #Sample Complexity Follow #Risk-Sensitive Follow #arXiv Follow #Recursive Entropy Follow