Finite Constant Frontiers and Auditable Regret Certificates for Average-Reward Reinforcement Learning

arXiv:2608.07725 · cs.LG · Submitted 2026-08-07 · Read on arXiv

Ibne Farabi Shihab, Abu Sa-Adat Mohamed Moon-Im Al Ahsan, Md Najmus Swaqeeb

cs.LG

Submitted: 2026-08-07

Updated: 2026-08-11

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Regret — 81× in this paper · explained in 4 episodes
• Reinforcement Learning — 26× in this paper · explained in 4 episodes
• Planner — 11× in this paper · explained in 3 episodes
• Regret Bound — 8× in this paper · explained in 2 episodes
• Regret Bounds — 7× in this paper · explained in 2 episodes
• Composition — 5× in this paper · explained in 2 episodes
• Consistency — 5× in this paper · explained in 3 episodes
• International Conference on Machine Learning — 4× in this paper
• Q-learning — 3× in this paper · explained in 3 episodes
• Generator — 2× in this paper · explained in 2 episodes
• Minimax Regret — 2× in this paper · explained in 4 episodes
• Regularization — 2× in this paper · explained in 3 episodes
• Sample Complexity — 2× in this paper · explained in 20 episodes
• Benchmark — 1× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• Gap-Dependent Regret — 1× in this paper · explained in 2 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• International Conference on Algorithmic Learning Theory — 1× in this paper
• Papers — 1× in this paper · explained in 3 episodes
• Trace — 1× in this paper · explained in 2 episodes
• Verification — 1× in this paper · explained in 2 episodes

Sources

• Exploration-Exploitation in Constrained MDPs
• Improved Analysis of UCRL2 with Empirical Bernstein Inequality

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