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\({\mathcal Q}\)-learning. (English) Zbl 0773.68062
Summary: \({\mathcal Q}\)-learning is a simple way for agents to learn how to act optimally in controlled Markovian domains. It amounts to an incremental method for dynamic programming which imposes limited computational demands. It works by successively improving its evaluations of the quality of particular actions at particular states.
The paper presents and proves in detail a convergence theorem for \({\mathcal Q}\)-learning based on that outlined in C. J. C. H. Watkins [Learning from delayed rewards. Ph.D. Thesis, University of Cambridge, England (1989)]. We show that \({\mathcal Q}\)-learning converges to the optimum action-values with probability 1 so long as all actions are repeatedly sampled in all states and the action-values are represented discretely. We also sketch extensions to the cases of non-discounted, but absorbing, Markov environments, and where many \({\mathcal Q}\) values can be changed each iteration, rather than just one.

MSC:
68T05 Learning and adaptive systems in artificial intelligence
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