Reinforcement learning with model sharing for multi-agent systems

Abstract

In this paper, a sharing method of model construction between multi-agents is presented to shorten the time of modeling. The sharing method allows the agents to share their knowledge in modeling. In the proposed method, the individual model held by each agent can be implemented with the heterogeneous structure such as decision tree. To decreasing the complexity of the sharing process, the proposed method executes model sharing between cooperative agents by means of the leaf nodes of trees instead of merging whole trees violently. The result of simulation in multi-agent cooperative domain illustrates that the proposed algorithm perform better than the one without sharing.