Q-learning is a model-free reinforcement learning algorithm that aims to learn the value of an action in a given state. It does this by updating a Q-value, which represents the expected future rewards for taking a particular action in a specific state, based on the experiences gained from interactions with the environment. This process allows an agent to make optimal decisions by balancing exploration and exploitation, thus facilitating learning even without a model of the environment.
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