Finite-Time Analysis of Asynchronous Stochastic Approximation and Q-Learning
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We consider a general asynchronous Stochastic Approximation (SA) scheme featuring a weighted infinity-norm contractive operator, and prove a bound on its finite-time convergence rate on a single trajectory. Additionally, we specialize the result to asynchronous Q-learning. The resulting bound matches the sharpest available bound for synchronous Q-learning, and improves over previous known bounds for asynchronous Q-learning.
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