For questions related to the Q-learning algorithm, which is a model-free and temporal-difference reinforcement learning algorithm that attempts to approximate the Q function, which is a function that, given a state s and an action a, returns a real number that represents the return (or value) of state s when action a is taken from s. Q-learning was introduced in the PhD thesis "Learning from Delayed Rewards" (1989) by Watkins.

For more info, see e.g. the book Reinforcement Learning: An Introduction (2nd edition) by Sutton and Barto. See also the related Wikipedia article or e.g. http://artint.info/html/ArtInt_265.html

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