Q-learning is a model-free reinforcement learning algorithm to learn quality of actions telling an agent what action to take under what circumstances. It does not require a model (hence the connotation “model-free”) of the environment, and it can handle problems with stochastic transitions and rewards, without requiring adaptations.
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.
Q-learning can identify an optimal action-selection policy for any given FMDP, given infinite exploration time and a partly-random policy.
“Q” names the function that the algorithm computes with the maximum expected rewards for an action taken in a given state.
