一种基于Agent团队的强化学习模型与应用研究

AN AGENT TEAM BASED REINFORCEMENT LEARNING MODEL AND ITS APPLICATION

多 Agent学习是近年来受到较多关注的研究方向 .以单 Agent强化学习 Q - learning算法为基础 ,提出了一种基于 Agent团队的强化学习模型 ,这个模型的最大特点是引入主导 Agent作为团队学习的主角 ,并通过主导Agent的角色变换实现整个团队的学习 .结合仿真机器人足球领域 ,设计了具体的应用模型 ,在几个方面对 Q -learning进行了扩充 ,并进行了实验 .

Multi agent learning has attracted increasing attention in recent years. In this paper, a novel model for reinforcement learning based on agent team is proposed. Its basis is Q learning, a single agent reinforcement learning algorithm. The most significant characteristic of the model is the introduction of the active agent, the major role in team learning. By switching the active agent, team learning is achieved. A model in robotic soccer domain is implemented by extending the Q learing algorithm, and some positive results are obtained in experiments. Success in robotic soccer domain shows the effectiveness of the model.