Abstract
Reinforcement learning is a technique developed for a single agent. If it's used for the cooperative behavior in multi-agent environment, one of the main problems is how to benefit from mutual interaction during the learning process. In this research, we propose an interactive learning system with cooperative ability through the interaction of reinforcement value among agents. In this method, when each agent repeats trial and error, the confidence degree between other agents could be generated and updated based on the degree of goal achievement and cooperation. The adoption strategy of reinforcement value is determined through the confidence degree. Each agent is able to adopt reinforcement value of others, and an interactive learning system can be built among agents. Therefore, each agent could learn the available experience from others. The cooperative behavior and group strategy of multi-agent is also learned through the interaction with environment and other agents.
