Q-learning

Model-free reinforcement learning algorithm / From Wikipedia, the free encyclopedia

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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 value of the total reward over any and all successive steps, starting from the current state.[2] Q-learning can identify an optimal action-selection policy for any given finite Markov decision process, given infinite exploration time and a partly random policy.[2] "Q" refers to the function that the algorithm computes – the expected rewards for an action taken in a given state.[3]

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