Applying assimilation and accommodation for cooperative learning of RoboCup agent
Jong-Yih Kuo, Hsuan-Kuei Cheng · 2010
Adapting learning is the essential ability to improve the convergence rate and learning quality in the multi-agent system. This paper integrates three adapting learning methods to make agent learns efficiently. Reinforcement learning is used to compute strategies for multi-agent soccer teams. The accommodation technology attaches the new knowledge from external information. As a conflict between the external knowledge and agent's knowledge, we utilize the assimilation technology to adjust the agent's knowledge. Finally, our method compares with UvA team and be verified on the RoboCup simulator.