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.
General
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