Multi-Agent Learning in Conflicting Multi-level Games with Incomplete Information

Coordination to some equilibrium point is an interesting problem in multi-agent reinforcement learning. In common interest single stage settings this problem has been studied profoundly and efficient solution techniques have been found. Also for particular multi-stage games some experiments show good results. However, for a large scale of problems the agents do not share a common pay-off function. Again, for single stage problems, a solution technique exists that finds a fair solution for all agents. In this paper we report on a technique that is based on learning automata theory and periodical policies. Letting pseudo-independent agents play periodical policies enables them to behave socially in pure conflicting multi-stage games as defined by E. Billard (Billard & Lakshmivarahan 1999; Zhou, Billard, & Lakshmivarahan 1999). We experimented with this technique on games where simple learning automata have the tendency not to cooperate or to show oscillating behavior resulting in a suboptimal pay-off. Simulation results illustrate that our technique overcomes these problems and our agents find a fair solution for both agents.