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This document discusses Q-learning, a reinforcement learning algorithm that enables agents to learn optimal actions in controlled Markovian environments without needing a model of the environment. It presents a convergence theorem proving that Q-learning will converge to optimal action-values with probability 1, provided all actions are sampled repeatedly across all states. The paper also outlines extensions to non-discounted Markov environments and discusses the learning process and conditions necessary fo

Author

Sunny

Language

EN