A Universal Framework for Reinforcement Learning

Warren B. Powell is Professor Emeritus at Princeton University, where he taught for 39 years, and is currently the Chief Analytics Officer at Optimal Dynamics. He is the founder and director of CASTLE Labs, which spans contributions to models and algorithms in stochastic optimization, with applications to energy systems, transportation, health, e-commerce, and the laboratory sciences (see www.castlelab.princeton.edu). He has pioneered the use of approximate dynamic programming for high-dimensional applications, and the knowledge gradient for active learning problems. His recent work has focused on developing a unified framework for sequential decision problems under uncertainty, spanning active learning to a wide range of dynamic resource allocation problems. He has authored books on Approximate Dynamic Programming and (with Ilya Ryzhov) Optimal Learning, and is nearing completion of a book Reinforcement Learning and Stochastic Optimization: A unified framework for sequential decisions.