Reinforcement Learning in the Multi-Robot Domain
- Maja J. Matarić
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This paper describes a formulation of reinforcement learning that enables learning in noisy, dynamic environments such as in the complex concurrent multi-robot learning domain. The methodology involves minimizing the learning space through the use of behaviors and conditions, and dealing with the credit assignment problem through shaped reinforcement in the form of heterogeneous reinforcement functions and progress estimators. We experimentally validate the approach on a group of four mobile robots learning a foraging task.
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- Reinforcement Learning in the Multi-Robot Domain
Volume 4, Issue 1 , pp 73-83
- Cover Date
- Print ISSN
- Online ISSN
- Kluwer Academic Publishers
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- robot learning
- group behavior
- multi-agent systems
- reinforcement learning
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- Maja J. Matarić (1)
- Author Affiliations
- 1. Volen Center for Complex Systems, Computer Science Department, Brandeis University, Waltham, MA, 02254