Fuzzy policy reinforcement learning in cooperative multi-robot systems

Gu, Dongbing and Yang, Erfu (2007) Fuzzy policy reinforcement learning in cooperative multi-robot systems. Journal of Intelligent and Robotic Systems, 48 (1). pp. 7-22. ISSN 0921-0296

Full text not available in this repository.Request a copy from the Strathclyde author

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.

Creators(s): Gu, Dongbing and Yang, Erfu ORCID logoORCID: https://orcid.org/0000-0003-1813-5950;
Item type:Article
ID code:53044
Keywords:cooperative control, flocking behavior, multi-agent reinforcement learning, policy gradient reinforcement learning, Engineering design, Control and Systems Engineering, Artificial Intelligence
Subjects:Technology > Engineering (General). Civil engineering (General) > Engineering design
Department:Faculty of Engineering > Design, Manufacture and Engineering Management
Depositing user: Pure Administrator
Date deposited:15 May 2015 15:36
Last modified:20 Jan 2021 22:05
Related URLs:
URI:https://strathprints.strath.ac.uk/id/eprint/53044
Export data:
CORE (COnnecting REpositories)