Q-Learning 0/1

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Q-learning is a model-free reinforcement learning algorithm that seeks to learn the optimal action-selection policy by iteratively updating the estimated value, or “Q-value,” of taking a specific action in a given state based on the received reward and the estimated value of future states. It does this by balancing exploration of new actions and exploitation of known rewards, eventually converging to the optimal policy that maximizes cumulative rewards over time.