Building collaboration in multi-agent systems using reinforcement learning

© Springer Nature Switzerland AG 2018. This paper presents a proof-of concept study for demonstrating the viability of building collaboration among multiple agents through standard Q learning algorithm embedded in particle swarm optimisation. Collaboration is formulated to be achieved among the agents via competition, where the agents are expected to balance their action in such a way that none of them drifts away of the team and none intervene any fellow neighbours territory, either. Particles are devised with Q learning for self training to learn how to act as members of a swarm and how to produce collaborative/collective behaviours. The produced experimental results are supportive to the proposed idea suggesting that a substantive collaboration can be build via proposed learning algorithm

Metaheuristic agent teams for job shop scheduling problems

This paper addresses and introduces an overview on various multi-agent architectures applied to teams of metaheuristic agents for job shop scheduling applications, whose developed and examined on distributed problem solving environments. We reported a couple of topologies; ATEAM is a centrally coordinating method, which provides very good results when well-studied, on the other hand, architectures based on peer-to-peer technology provide wider flexibility in implementing various fashions. The experimentation for each targeted topology has revealed more details and attracts more attentions