New research details $O(1/n)$ convergence rate for RVI Q-learning

Researchers have established a finite-time convergence guarantee for synchronous relative value iteration (RVI) Q-learning in finite weakly communicating Markov decision processes (MDPs). The study details convergence rates for stochastic iterations under specific noise conditions, yielding an $O(n^{-1})$ bound on the mean-squared Bellman span residual of RVI Q-learning iterates. This work demonstrates that $O(\epsilon^{-1})$ synchronous iterations are sufficient for an $\epsilon$-optimal policy in expectation, with instance-dependent constants. AI

IMPACT Provides theoretical underpinnings for reinforcement learning algorithms, potentially improving efficiency in decision-making systems.

RANK_REASON Academic paper detailing theoretical convergence guarantees for a specific machine learning algorithm. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• Markov decision processes
• RVI Q-learning

• paper
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