A reinforcement learning algorithm for building collaboration in multi-agent systems
This is an incremental study for multi-agent systems, focusing on collaboration via competition.
The paper tackled the problem of building collaboration in multi-agent systems by embedding standard Q-learning in particle swarm optimization, resulting in supportive evidence that substantive collaboration can be achieved through this approach.
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 some sort 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. Particles are devised with Q learning algorithm for self training to learn how to act as members of a swarm and how to produce collaborative/collective behaviours. The produced results are supportive to the algorithmic structures suggesting that a substantive collaboration can be build via proposed learning algorithm.