Fuzzy Policy Reinforcement Learning in Cooperative Multi-robot Systems |
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Authors: | Dongbing Gu Erfu Yang |
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Affiliation: | (1) Department of Computer Science, University of Essex, Wivenhoe Park, Colchester Essex, CO4 3 SQ, UK |
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Abstract: | A multi-agent reinforcement learning algorithm with fuzzy policy is addressed in this paper. This algorithm is used to deal
with some control problems in cooperative multi-robot systems. Specifically, a leader-follower robotic system and a flocking
system are investigated. In the leader-follower robotic system, the leader robot tries to track a desired trajectory, while
the follower robot tries to follow the reader to keep a formation. Two different fuzzy policies are developed for the leader
and follower, respectively. In the flocking system, multiple robots adopt the same fuzzy policy to flock. Initial fuzzy policies
are manually crafted for these cooperative behaviors. The proposed learning algorithm finely tunes the parameters of the fuzzy
policies through the policy gradient approach to improve control performance. Our simulation results demonstrate that the
control performance can be improved after the learning. |
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Keywords: | cooperative control flocking behavior multi-agent reinforcement learning policy gradient reinforcement learning |
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