A unified analysis of value-function-based reinforcement- learning algorithms.
Reinforcement learning is the problem of generating optimal behavior in a sequential decision-making environment given the opportunity of interacting with it. Many algorithms for solving reinforcement-learning problems work by computing improved estimates of the optimal value function. We extend prior analyses of reinforcement-learning algorithms and present a powerful new theorem that can provide a unified analysis of such value-function-based reinforcement-learning algorithms. The usefulness of the theorem lies in how it allows the convergence of a complex asynchronous reinforcement-learning algorithm to be proved by verifying that a simpler synchronous algorithm converges. We illustrate the application of the theorem by analyzing the convergence of Q-learning, model-based reinforcement learning, Q-learning with multistate updates, Q-learning for Markov games, and risk-sensitive reinforcement learning.
Szepesvári
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Mindmaker, Ltd., Budapest 1121, Konkoly Thege M. U. 29-33, Hungary.
Littman
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D000465:Algorithms; D001185:Artificial Intelligence; D007858:Learning; D012054:Reinforcement, Psychology