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Nội dung
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Learning Motor Skills : From Algorithms to Robot Experiments / Jens Kober; Jan Peters
Cham : Springer, 2014.
191 pages. : illustrations ; 24 cm.
Ký hiệu phân loại (DDC): 006.31
This book presents the state of the art in reinforcement learning applied to robotics both in terms of novel algorithms and applications. It discusses recent approaches that allow robots to learn motor skills and presents tasks that need to take into account the dynamic behavior of the robot and its environment, where a kinematic movement plan is not sufficient. The book illustrates a method that learns to generalize parameterized motor plans which is obtained by imitation or reinforcement learning, by adapting a small set of global parameters, and appropriate kernel-based reinforcement learning algorithms. The presented applications explore highly dynamic tasks and exhibit a very efficient learning process. All proposed approaches have been extensively validated with benchmarks tasks, in simulation, and on real robots. These tasks correspond to sports and games but the presented techniques are also applicable to more mundane household tasks. The book is based on the first author's doctoral thesis, which won the 2013 EURON Georges Giralt PhD Award.
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Reinforcement learning : an introduction / Richard S. Sutton, Andrew G. Barto
Massachusetts : The MIT Press, 2020
xxii, 526 pages. : illustrations (some color) ; 24 cm.
Ký hiệu phân loại (DDC): 006.31
Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms.
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