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报告题目：Reinforcement Learning and Multi-agent Scenarios

报告人：王剑虹博士

单位：英国帝国理工学院

时间：2019年9月25日周三下午14:00

地点：中心楼二楼js33333金沙线路检测教育部重点实验室会议室

邀请人/主持人：陈杨杨副教授

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欢迎各位老师和研究生参加！

报告摘要：

Reinforcement Learning is a kind of learning strategy other than Supervised Learning and Unsupervised Learning in Machine Learning context. Because of the recent success of Reinforcement Learning on single-agent games, e.g., Atari games and Go, researchers start exploring the possibility of multi-agent scenarios. The main problems of Multi-agent Reinforcement Learning lie on the dynamic adaption of the environment (including other agents) and the communication with other agents. To evaluate the performance of dynamic adaption (saying finding a stationary state of the whole system), game theory is applied and Nash equilibrium is usually utilised as a criterion. Although the model in game theory can guarantee the convergence of the system, the strong assumption makes it useless in real applications. Communication is a method that enables cooperation or coordination by gathering the local information of agents. The main issue is the communication protocol in the real world and the selection of communication targets. For these reasons, we can see Multi-agent Reinforcement Learning is still a challenge and deserves to be studied.

报告人简介：

Jianhong Wang is a PhD candidate studying in Imperial College London, who is interested in Reinforcement Learning and Multi-agent Learning.