A HYBRID MULTIAGENT REINFORCEMENT LEARNING APPROACH USING STRATEGIES AND FUSION

DOI Web Site 1 Citations
  • IOANNIS Partalas
    Department of Informatics, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
  • IOANNIS FENERIS
    Department of Informatics, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
  • IOANNIS VLAHAVAS
    Department of Informatics, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece

Description

<jats:p>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.</jats:p>

Journal

Citations (1)*help

See more

Keywords

Details 詳細情報について

Report a problem

Back to top