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...
Many real-world applications of multiagent systems require independently designed (heterogeneous) and operated (autonomous) agents to interoperate. We consider agents who offer bu...
Matteo Baldoni, Cristina Baroglio, Amit K. Chopra,...
Cooperative games provide an appropriate framework for fair and stable resource allocation in multiagent systems. This paper focusses on monotone cooperative games, a class which ...
Multi-Agent Agreement problems (MAP) - the ability of a population of agents to search out and converge on a common state - are central issues in many multi-agent settings, from d...
A collective of agents often needs to maximize a “world utility” function which rates the performance of an entire system, while subject to communication restrictions among th...