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Reinforcement learning is an adaptive and flexible control method for autonomous system. In our previous works, we had proposed a reinforcement learning algorithm for redundant systems: "Q-learning with dynamic structuring of exploration space based on GA (QDSEGA)", and applied it to multi-agent systems. However previous works of the QDSEGA have been restricted to homogeneous agents. In this paper, we extend our previous works of multi-agent systems, and propose a hybrid autonomous control method for heterogeneous multi-agent systems. To demonstrate the effectiveness of the proposed method, simulations of transportation task by 10 heterogeneous mobile robots have been carried out. As a result effective behaviors have been obtained.
learning (artificial intelligence)
Digital Object Identifier: 10.1109/IROS.2003.1249245
Published with permission from the copyright holder.this is the institute's copy, as published in Intelligent Robots and Systems, 2003. (IROS 2003). Proceedings. 2003 IEEE/RSJ International Conference on, 27-31 Oct. 2003, Volume 3, Pages 2500-2505.
Copyright © 2003 IEEE. All rights reserved.
Intelligent Robots and Systems