Q-learning is a model-free reinforcement learning algorithm that helps an agent learn how to optimally make decisions by estimating the value of actions taken in different states. It does this through a process of exploration and exploitation, where the agent tries various actions to discover their outcomes and updates its knowledge accordingly. This learning process is particularly useful in adaptive control systems where the environment may change, allowing for the continuous improvement of decision-making policies.
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