期刊
INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH
卷 32, 期 11, 页码 1238-1274出版社
SAGE PUBLICATIONS LTD
DOI: 10.1177/0278364913495721
关键词
Reinforcement learning; learning control; robot; survey
类别
资金
- European Community [ICT-248273 GeRT, ICT-270327 CompLACS]
- Defense Advanced Research Project Agency through Autonomous Robotics Manipulation-Software
- Office of Naval Research Multidisciplinary University Research Initiatives Distributed Reasoning in Reduced Information Spaces and Provably Stable Vision-Based Control of High-Speed Flight through Forests and Urban Environments
Reinforcement learning offers to robotics a framework and set of tools for the design of sophisticated and hard-to-engineer behaviors. Conversely, the challenges of robotic problems provide both inspiration, impact, and validation for developments in reinforcement learning. The relationship between disciplines has sufficient promise to be likened to that between physics and mathematics. In this article, we attempt to strengthen the links between the two research communities by providing a survey of work in reinforcement learning for behavior generation in robots. We highlight both key challenges in robot reinforcement learning as well as notable successes. We discuss how contributions tamed the complexity of the domain and study the role of algorithms, representations, and prior knowledge in achieving these successes. As a result, a particular focus of our paper lies on the choice between model-based and model-free as well as between value-function-based and policy-search methods. By analyzing a simple problem in some detail we demonstrate how reinforcement learning approaches may be profitably applied, and we note throughout open questions and the tremendous potential for future research.
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