AI Research
Finite Constant Frontiers and Auditable Regret Certificates for Average-Reward Reinforcement Learning
Bridging the gap in RL performance metrics through standardized, auditable regret certificates.
Average-reward reinforcement-learning regret is known up to logarithmic factors, but the numerical content of published guarantees is difficult to compare because probability mode, structural parameter, logarithmic normalization, prior information, and planning assumptions differ...
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ArXiv CS.LG
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ArXiv CS.LG
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By standardizing the evaluation of reinforcement learning algorithms, this research enables a more rigorous, apples-to-apples comparison of performance guarantees. The introduction of auditable, finite certificates provides a crucial step toward building provably reliable and transparent autonomous agents.
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