A reinforcement learning algorithm for building collaboration in
  multi-agent systems
  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.
