Reinforcement learning and approximate dynamic programming for feedback control - Lewis, Frank L; Liu, Derong, 1963-; IEEE Xplore (Online Service); John Wiley & Sons (Hoboken, New Jersey : John Wiley & Sons, Inc., 2013)
"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"--
"Reinforcement learning and adaptive control can be useful for controlling a wide variety of systems including robots, industrial processes, and economical decision making"--
23.6 Conclusions 532 -- 24. Feature Selection for Neuro-Dynamic Programming 535 -- Dayu Huang, W. Chen, P. Mehta, S. Meyn, and A. Surana -- 24.1 Introduction 535 -- 24.2 Optimality Equations 536 -- 24.3 Neuro-Dynamic Algorithms 542 -- 24.4 Fluid Models 551 -- 24.5 Diffusion Models 554 -- 24.6 Mean Field Games 556 -- 24.7 Conclusions 557 -- 25. Approximate Dynamic Programming for Optimizing Oil Production 560 -- Zheng Wen, Louis J. Durlofsky, Benjamin Van Roy, and Khalid Aziz -- 25.1 Introduction 560 -- 25.2 Petroleum Reservoir Production Optimization Problem 562 -- 25.3 Review of Dynamic Programming and Approximate Dynamic Programming 564 -- 25.4 Approximate Dynamic Programming Algorithm for Reservoir Production Optimization 566 -- 25.5 Simulation Results 573 -- 25.6 Concluding Remarks 578 -- 23.6 Conclusions 532 -- 24. Feature Selection for Neuro-Dynamic Programming 535 -- Dayu Huang, W. Chen, P. Mehta, S. Meyn, and A. Surana -- 24.1 Introduction 535 -- 24.2 Optimality Equations 536 -- 24.3 Neuro-Dynamic Algorithms 542 -- 24.4 Fluid Models 551 -- 24.5 Diffusion Models 554 -- 24.6 Mean Field Games 556 -- 24.7 Conclusions 557 -- 25. Approximate Dynamic Programming for Optimizing Oil Production 560 -- Zheng Wen, Louis J. Durlofsky, Benjamin Van Roy, and Khalid Aziz -- 25.1 Introduction 560 -- 25.2 Petroleum Reservoir Production Optimization Problem 562 -- 25.3 Review of Dynamic Programming and Approximate Dynamic Programming 564 -- 25.4 Approximate Dynamic Programming Algorithm for Reservoir Production Optimization 566 -- 25.5 Simulation Results 573 -- 25.6 Concluding Remarks 578 -- 26. A Learning Strategy for Source Tracking in Unstructured Environments 582 -- Titus Appel, Rafael Fierro, Brandon Rohrer, Ron Lumia, and John Wood -- 26.1 Introduction 582 -- 26.2 Reinforcement Learning 583 -- 26.3 Light-Following Robot 589 -- 26.4 Simulation Results 592 -- 26.5 Experimental Results 595 -- 26.6 Conclusions and Future Work 599 -- References 599 -- INDEX 601.
In Wiley online library
Includes bibliographical references.
Also available in print.
Mode of access: World Wide Web
Description based on PDF viewed 12/22/2015.
Keywords: Reinforcement learning; Feedback control systems; Electronic books