Q-learning

Q-learning is a model-free reinforcement learning algorithm used to find the optimal action-selection policy using a Q-function. The Q-function represents the expected cumulative reward obtained from taking a particular action in a given state and following the optimal policy thereafter. The algorithm iteratively updates the Q-values of state-action pairs using the Bellman equation until convergence. Q-learning is a popular algorithm for solving complex decision-making problems in various fields, including robotics, game theory, and finance.
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