A multiagent fuzzy policy reinforcement learning algorithm with application to leader-follower robotic systems

Yang, Erfu and Gu, Dongbing; (2006) A multiagent fuzzy policy reinforcement learning algorithm with application to leader-follower robotic systems. In: 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, GBR, pp. 3197-3202. ISBN 1424402581 (https://doi.org/10.1109/IROS.2006.282421)

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Abstract

A multiagent reinforcement learning algorithm with fuzzy policy is addressed in this paper for dealing with the learning and control issues in cooperative multiagent systems with continuous states and actions, particularly for autonomous robotic formation systems. The parameters of fuzzy policy are finely tuned by the gradient multiagent reinforcement learning algorithm to improve the overall performance of an initial controller (policy). A leader-follower robotic system is chosen as a platform to benchmark the performance of the multiagent fuzzy policy reinforcement learning algorithm. Our simulation results demonstrate that the control performance can be improved in many aspects. This work also can be seen as a scaling up of currently popular multiagent reinforcement learning to the robotic domain with continuous state and action space as well as high dimensionality.

ORCID iDs

Yang, Erfu ORCID logoORCID: https://orcid.org/0000-0003-1813-5950 and Gu, Dongbing;
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