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Title: A Hybrid Multiagent Reinforcement Learning Approach using Strategies and Fusion
Author(s): I. Partalas, I. Feneris, I. Vlahavas.
Availability: Click here to download the PDF (Acrobat Reader) file (18 pages).
Keywords: multi-agent reinforcement learning.
Appeared in: International Journal of Artificial Intelligence Tools (IJAIT), World Scientific, 17 (5), pp. 945-961, 2008.
Abstract: Reinforcement Learning comprises an attractive solution to the problem of coordinating a group of agents in a Multiagent System, due to its robustness for learning in uncertain and unknown environments. This paper proposes a multiagent Reinforcement Learning approach, that uses coordinated actions, which we call strategies and a fusing process to guide the agents. To evaluate the proposed approach, we conduct experiments in the Predator-Prey domain and compare it with other learning techniques. The results demonstrate the efficiency of the proposed approach.
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