Q-learning
CJCH Watkins, P Dayan - Machine learning, 1992 - Springer
CJCH Watkins, P Dayan
Machine learning, 1992•SpringerAbstract Q-learning (Watkins, 1989) 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. This paper
presents and proves in detail a convergence theorem for Q-learning based on that outlined
in Watkins (1989). We show that Q-learning converges to the optimum action-values with …
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. This paper
presents and proves in detail a convergence theorem for Q-learning based on that outlined
in Watkins (1989). We show that Q-learning converges to the optimum action-values with …
Abstract
Q-learning (Watkins, 1989) 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.
This paper presents and proves in detail a convergence theorem forQ-learning based on that outlined in Watkins (1989). We show thatQ-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 manyQ values can be changed each iteration, rather than just one.
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