Abstract In this paper we address the problem of simultaneous learning and coordination in multiagent Markov decision problems (MMDPs) with infinite state-spaces. We separate this ...
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
In this paper, we investigate Reinforcement learning (RL) in multi-agent systems (MAS) from an evolutionary dynamical perspective. Typical for a MAS is that the environment is not ...
Karl Tuyls, Pieter Jan't Hoen, Bram Vanschoenwinke...
Embedded systems consisting of collaborating agents capable of interacting with their environment are becoming ubiquitous. It is crucial for these systems to be able to adapt to t...
A MAS architecture consisting of service centers is proposed. Within each service center, a mediator coordinates service delivery by allocating individual tasks to corresponding t...