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Near-Minimax-Optimal Rates for Asynchronous TD Learning with Markovian Data
We study the last iterate of standard importance-weighted temporal-difference (TD) learning from a single trajectory of a finite Markov decision process. The trajectory follows a behavior policy $b$ with an irreducible, possibly periodic state chain; a target policy $π$ covered by $b$ is evaluated w
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