Title

Multi-Agent Coordination Method Based On Fuzzy Q-Learning

Keywords

Fuzzy Q-learning; Multi-agent coordination; Multi-agent system; RoboCup

Abstract

Traditional Reinforcement learning algorithm can only solve the learning problem of the intelligent agent with discrete state space and discrete action space. This paper studies the coordination of multiple intelligent agents in a complicated dynamic environment with uncertainty. A coordination model based on the fuzzy Q-learning technique is suggested. This model uses fuzzy logic to generalize the agent's continuous state space. Every agent, when making decisions on its actions, needs to consider the influences of other agents to the environment. The agent first evaluates the actions they select, then, uses the fuzzy Q-learning to learn their action strategy. In the process, the action keeps improving and the conflicts among agents can be resolved. This model was used in the RoboCup soccer simulation game and the simulation results showed that the performance of attacking is obviously improved. © 2008 IEEE.

Publication Date

9-25-2008

Publication Title

Proceedings of the World Congress on Intelligent Control and Automation (WCICA)

Number of Pages

5401-5405

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/WCICA.2008.4593811

Socpus ID

52149104259 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/52149104259

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