Reinforcement Learning
Reinforcement learning is a body of theory and algorithms for optimal decision making developed within the machine learning and operations research communities in the last twenty-five years, and which have separately become important in psychology and neuroscience. Reinforcement learning methods find useful approximate solutions to optimal-control problems that are too large or too ill-defined for classical methods such as dynamic programming. For example, reinforcement-learning methods have obtained the best-known solutions in such diverse automation applications as helicopter flying, elevator scheduling, playing backgammon, and resource-constrained scheduling.
Reinforcement learning researchers at the ß÷ßäÉçÇø seek to create new methods for reinforcement learning that remove some of the limitations on its widespread application and to develop reinforcement learning as a model of intelligence that could approach human abilities. These objectives are pursued through mathematics, through computational experiments, through applications in robotics, game-playing, and other areas, and through the development of computational models of natural learning processes.