This overview by an award-winning researcher of the ways reinforcement learning can be applied to robotics includes new algorithms and applications. It assesses their success in benchmark tasks such as darts, table tennis, and ball-throwing and bouncing.

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.

Presents an overview of reinforcement learning as applied to robotics Provides novel algorithms and novel applications for learning motor skills Extensively evaluates the applications of the approaches on benchmark and robot tasks (including ball-in-a-cup, darts, table-tennis, throwing and ball-bouncing) with simulated and real robots

Inhalt
Reinforcement Learning in Robotics: A Survey.- Movement Templates for Learning of Hitting and Batting.- Policy Search for Motor Primitives in Robotics.- Reinforcement Learning to Adjust Parameterized Motor Primitives to New Situations.- Learning Prioritized Control of Motor Primitives.