Abstract

<p>Policy search is a subfield in reinforcement learning which focuses onfinding good parameters for a given policy parametrization. It is wellsuited for robotics as it can cope with high-dimensional state and actionspaces, one of the main challenges in robot learning. We review recentsuccesses of both model-free and model-based policy search in robotlearning.Model-free policy search is a general approach to learn policiesbased on sampled trajectories. We classify model-free methods based ontheir policy evaluation strategy, policy update strategy, and explorationstrategy and present a unified view on existing algorithms. Learning apolicy is often easier than learning an accurate forward model, and,hence, model-free methods are more frequently used in practice. However,for each sampled trajectory, it is necessary to interact with the* Both authors contributed equally.robot, which can be time consuming and challenging in practice. Modelbasedpolicy search addresses this problem by first learning a simulatorof the robot’s dynamics from data. Subsequently, the simulator generatestrajectories that are used for policy learning. For both modelfreeand model-based policy search methods, we review their respectiveproperties and their applicability to robotic systems.</p>

Keywords

Reinforcement learningArtificial intelligenceRoboticsComputer scienceRobotMachine learningTrajectoryAction (physics)Policy learning

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Publication Info

Year
2011
Type
book
Volume
2
Issue
1-2
Pages
1-142
Citations
679
Access
Closed

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Marc Peter Deisenroth, Gerhard Neumann, Jan Peters (2011). A Survey on Policy Search for Robotics. Foundations and Trends in Robotics , 2 (1-2) , 1-142. https://doi.org/10.1561/9781601987037

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DOI
10.1561/9781601987037