Expertness based cooperative Q-learning

By using other agents' experiences and knowledge, a learning agent may learn faster, make fewer mistakes, and create some rules for unseen situations. These benefits would be gained if the learning agent can extract proper rules out of the other agents' knowledge for its own requirements. One possible way to do this is to have the learner assign some expertness values (intelligence level values) to the other agents and use their knowledge accordingly. In this paper, some criteria to measure the expertness of the reinforcement learning agents are introduced. Also, a new cooperative learning method, called weighted strategy sharing (WSS) is presented. In this method, each agent measures the expertness of its teammates and assigns a weight to their knowledge and learns from them accordingly. The presented methods are tested on two Hunter

• Type: Journal
• Year: 2002
• Where: TSMC
• Authors: Majid Nili Ahmadabadi, Masoud Asadpour
• Keywords: Cooperative Learning Method, Fewer Mistakes, TSMC 2002, Unseen Situations