MLKD logo   Machine Learning &
Knowledge Discovery Group

Publication Details

  Author(s): I. Partalas, I. Feneris, I. Vlahavas.

Title: “A Hybrid Multiagent Reinforcement Learning Approach using Strategies and Fusion”.

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.

Relevant Links: