Reinforcement signal communication based multiagent reinforcement learning

摘要

Reinforcement learning is the major learning mechanism for an agent to adapt itself to various situations flexibly. However, in a multiagent system environment that has mutual dependency among agents, it Is difficult for a human to setup suitable learning goals for each agent. Therefore, it requires the active and interactive learning function that treats how to coordinate the interaction among other learning agents. This paper presents a new framework of multiagent reinforcement learning to generate and coordinate each learning goal interactively among agents. To realize this, it presents to treat each learning goal as a reinforcement signal that can be communicated among agents. Then the issues of the self-generation of goals and evaluation criteria are discussed.