Abstract
Multi-agent reinforcement learning features many problems, one of these being state-space explosion based on combinations of policies that each agent has. Generally, if agents can share their policies, they can effectively search their enormous state space; that, however, simultaneously produces a risk of local optimality. Hence we propose a novel policy-sharing system based on the Learning Classifier System, on which agents locally share their policies. The aim of this system is to decrease the probability of falls into local optimality, and to effectively reduce state space by policy sharing. To verify the above, we use simplified soccer, which has discrete space and time.
