Q-learning is a model-free reinforcement learning algorithm to learn the value of an action in a particular state. It does not require a model of the environment (hence "model-free"), and it can handle problems with stochastic transitions and rewards without requiring adaptations.

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For any finite Markov decision process (FMDP), 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.[1] Q-learning can identify an optimal action-selection policy for any given FMDP, given infinite exploration time and a partly-random policy.[1] "Q" refers to the function that the algorithm computes – the expected rewards for an action taken in a given state.[2]