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Q-Learning

Q-learning, introduced by Christopher Watkins and Peter Dayan in 1992, is a model-free reinforcement-learning algorithm that learns the value of taking each action in each state — the Q-function — purely from experience, without a model of the environment. It is off-policy: it learns the optimal action-values while following an exploratory behaviour policy, and under standard conditions it provably converges to the optimal policy.

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Q-Learning (Off-Policy Temporal-Difference Control)

Taksonomisk metoderegister · ml-model / machine-learning

Watkins, C. J. C. H., & Dayan, P. (1992). Q-learning. Machine Learning, 8(3–4), 279–292. · DOI 10.1007/BF00992698
Sutton, R. S., & Barto, A. G. (2018). Reinforcement Learning: An Introduction (2nd ed.). MIT Press. · ISBN 978-0-262-03924-6

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Same method familyDeep Reinforcement Learningmachine-suggested · Relational suggestion, not evidence.See alsoDynamic Programmingmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketPolicy Gradientmachine-suggested · Relational suggestion, not evidence.