Distributionally Robust Average-Reward Reinforcement Learning: Finite-Sample Guarantees under Weak Communication

arXiv:2609.32727 · cs.LG, math.OC, stat.ML · Submitted 2026-09-26 · Read on arXiv

cs.LG, math.OC, stat.ML

Submitted: 2026-09-26

Updated: 2026-09-26

Terminology

• Reinforcement Learning — 25× in this paper · explained in 4 episodes
• Sample Complexity — 21× in this paper · explained in 15 episodes
• Q-learning — 10× in this paper · explained in 3 episodes
• Generative Model — 4× in this paper · explained in 2 episodes
• Homogeneity — 3× in this paper · explained in 2 episodes
• Regret — 2× in this paper · explained in 3 episodes
• Reward Function — 2× in this paper · explained in 2 episodes
• Verification — 2× in this paper · explained in 2 episodes
• Wasserstein Distance — 2× in this paper · explained in 4 episodes
• Action Space — 1× in this paper · explained in 1 episode
• Actor — 1× in this paper · explained in 3 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Chain-of-Thought Prompting — 1× in this paper · explained in 2 episodes
• Convergence Guarantees — 1× in this paper · explained in 3 episodes
• Core — 1× in this paper · explained in 2 episodes
• Deep Reinforcement Learning — 1× in this paper · explained in 2 episodes
• Kullback-Leibler (KL) Divergence — 1× in this paper · explained in 3 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Minimax optimality — 1× in this paper · explained in 2 episodes
• Nash Equilibrium — 1× in this paper · explained in 2 episodes
• Reinforcement Learning (RL) — 1× in this paper · explained in 25 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Theoretical Guarantees — 1× in this paper · explained in 2 episodes

Sources

• Learning Policy from a Single Trajectory in Average-Reward Markov Decision Process
• On Convergence of Average-Reward Q-Learning in Weakly Communicating Markov Decision Processes
• Bellman Optimality of Average-Reward Robust Markov Decision Processes with a Constant Gain
• Robust Average-Reward Markov Decision Processes: Minimax-Optimal Learning via Plug-in Reductions

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