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Stochastic approximation with cone-contractive operators: Sharp ell_infty-bounds for Q-learning
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Stochastic approximation with cone-contractive operators: Sharp ell_infty-bounds for Q-learning
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Motivated by the study of $Q$-learning algorithms in reinforcement learning, we study a class of stochastic approximation procedures based on operators that satisfy monotonicity and quasi-contractivity conditions with respect to an underlying cone. We prove a general sandwich relation on the iterate error at each time, and use it to derive non-asymptotic bounds on the error in terms of a cone-induced gauge norm. These results are derived within a deterministic framework, requiring no assumptions on the noise. We illustrate these general bounds in application to synchronous $Q$-learning for discounted Markov decision processes with discrete state-action spaces, in particular by deriving non-asymptotic bounds on the $\ell_\infty$-norm for a range of stepsizes. These results are the sharpest known to date, and we show via simulation that the dependence of our bounds cannot be improved in a worst-case sense. These results show that relative to a model-based $Q$-iteration, the $\ell_\infty$-based sample complexity of $Q$-learning is suboptimal in terms of the discount factor $\gamma$.
Forward citations
Cited by 12 Pith papers
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Establishes Õ(1/k) mean-square last-iterate convergence for asynchronous average-reward Q-learning with adaptive stepsizes and proves adaptivity is necessary.
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