: Growing importance of distributed data mining techniques has recently attracted attention of researchers in multiagent domain. Several agent-based application have been already c...
Jan Tozicka, Michael Rovatsos, Michal Pechoucek, S...
This paper describes an algorithm, called CQ-learning, which learns to adapt the state representation for multi-agent systems in order to coordinate with other agents. We propose ...
This paper proposes the β-WoLF algorithm for multiagent reinforcement learning (MARL) in the stochastic games framework that uses an additional “advice” signal to inform agen...
— Humanoid robots are routinely engaged in tasks requiring the coordination between multiple degrees of freedom and sensory inputs, often achieved through the use of sensorymotor...
Multi-Agent Reinforcement Learning (MARL) algorithms suffer from slow convergence and even divergence, especially in largescale systems. In this work, we develop an organization-b...
Chongjie Zhang, Sherief Abdallah, Victor R. Lesser