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Extend Single-agent Reinforcement Learning Approach to a Multi-robot Cooperative Task in an Unknown Dynamic Environment

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2 Author(s)
Ying Wang ; Mechanical Engineer Department, The University of British Columbia, Vancouver, BC, V6T 1N3, Canada. phone: 604-822-4850; e-mail: ywang@mech.ubc.ca ; C. W. de Silva

Machine learning technology helps multi-robot systems to carry out desired tasks in an unknown dynamic environment. In this paper, we extend the single-agent Q-learning algorithm to a multi-robot box-pushing system in an unknown dynamic environment with random obstacle distribution. There are two kinds of extensions available: directly extending MDP (Markov decision process) based Q-learning to the multi-robot domain, and SG-based (stochastic game based) Q-learning. Here, we select the first kind of extension because of its simplicity. The learning space, the box dynamics, and the reward function etc. are presented in this paper. Furthermore, a simulation system is developed and its results show effectiveness, robustness and adaptivity of this learning-based multi-robot system. Our statistical analysis of the results also shows that the robots learned correct cooperative strategy even in a dynamic environment.

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The 2006 IEEE International Joint Conference on Neural Network Proceedings

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