RLAI research program pursues an approach to artificial-intelligence and engineering problems in which they are formulated as large optimal-control problems and approximately solved using reinforcement-learning methods. Reinforcement learning is a new body of theory and techniques for optimal control that has been developed in the last twenty years primarily within the machine learning and operations research communities, and which have separately become important in psychology and neuroscience. Reinforcement learning researchers have developed novel methods to approximate solutions to optimal-control problems that are too large or too ill-defined for classical solution 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.
The objectives of the RLAI research program are 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 the development of robotic systems, and through the development and testing of computational models of natural learning processes.