Q-learning is a model-free reinforcement learning algorithm to learn the value of an action in a particular state. It does not require a model of the environment (hence "model-free"), and it can handle problems with stochastic transitions and rewards without requiring adaptations.[1] For any finite Markov decision process, Q-learning finds an optimal policy in the sense of maximizing the expected
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