Q-learning is a model-free reinforcement learning technique.
Q-learning is a model-free, on-policy reinforcement learning technique that aims to find an action-value function that gives the expected utility (reinforcement) of taking a given action in a given state and following a fixed policy thereafter.
One of the strengths of Q-learning is that it needs only a reinforcement function to be given (i.e. a function which tells how well, or how bad the agent is performing). During the learning process, the agent needs to balance exploitation (acting greedily in terms of current action-value function) vs exploration (action randomly to discover new states or better actions then currently estimated). A common simple example for handling this issue is using an epsilon-greedy policy.
