Background and Motivation
|Reinforcement learning (RL) represents the most promising framework for designing intelligent machines. As a biologically-inspired paradigm, RL offers the potential to build sub-optimal decision making systems that operate under partial and imperfect observations. |
|Our research focuses on the use of function approximation techniques in adaptive dynamic programming frameworks, to design systems that scale in both size and speed. In particular, we study effective solutions for solving Partially-Observable Markov Decision Processes (POMDPs), which correspond to a broad range of real-life stochastic control problems.