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Q-learning is a type of reinforcement learning that enables a model to learn and improve over time by taking the correct action. It is a model-free approach that does not require a model of the environment and takes an off-policy approach to reinforcement learning. This is achieved by using Q-values, or action values, which are stored in a Q-table and are used to determine the optimal action based on the current state.
