Reinforcement Learning and Approximate Dynamic Programming for Feedback Control

Reinforcement learning (RL) and adaptive dynamic programming (ADP) has been one of the most critical research fields in science and engineering for modern complex systems. This book describes the latest RL and ADP techniques for decision and control in human engineered systems, covering both single player decision and control and multi-player games. Edited by the pioneers of RL and ADP research, the book brings together ideas and methods from many fields and provides an important and timely guidance on controlling a wide variety of systems, such as robots, industrial processes, and economic decision-making.

Keywords: Intelligent Systems & Agents, reinforcement learning and approximate dynamic programming for feedback control, reinforcement learning, adaptive control, stochastic learning, function approximators, frank lewis, derong liu, cao, busoniuReinforcement learning, approximate dynamic programming, optimal control systems, adaptive dynamic programming, policy iteration, value iteration