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Q-learning is a reinforcement learning algorithm that trains an agent to assign values to its possible actions based on its current state, without requiring a model of the environment (model-free). It can handle problems with stochastic transitions and rewards without requiring adaptations.^[1]

For example, in a grid maze, an agent learns to reach an exit worth 10 points. At a junction, Q-learning might assign a higher value to moving right than left if right gets to the exit faster, improving this choice by trying both directions over time.

For any finite Markov decision process, Q-learning finds an optimal policy in the sense of maximizing the expected value of the total reward over any and all successive steps, starting from the current state.^[2] Q-learning can identify an optimal action-selection policy for any given finite Markov decision process, given infinite exploration time and a partly random policy.^[2]

"Q" refers to the function that the algorithm computes: the expected reward—that is, the quality—of an action taken in a given state.^[3]

^ Li, Shengbo (2023). Reinforcement Learning for Sequential Decision and Optimal Control (First ed.). Springer Verlag, Singapore. pp. 1–460. doi:10.1007/978-981-19-7784-8. ISBN 978-9-811-97783-1. S2CID 257928563.{{cite book}}: CS1 maint: location missing publisher (link)
^ ^a ^b Melo, Francisco S. "Convergence of Q-learning: a simple proof" (PDF).
^ Matiisen, Tambet (December 19, 2015). "Demystifying Deep Reinforcement Learning". neuro.cs.ut.ee. Computational Neuroscience Lab. Retrieved 2018-04-06.