Paper Details

  • Title:

    A Hybrid Multiagent Reinforcement Learning Approach using Strategies and Fusion

  • Author(s):

    I. Partalas, I. Feneris, I. Vlahavas

  • Keywords: multi-agent reinforcement learning.
  • 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.

  • Category: Journal Papers
  • Tags: 2008 Partalas Feneris Vlahavas