© 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.
Aydin, M. E., & Fellows, R. (2018). Building collaboration in multi-agent systems using reinforcement learning. Lecture Notes in Artificial Intelligence, 11056 LNAI, 201-212. https://doi.org/10.1007/978-3-319-98446-9_19