报告题目:复杂优化的多agent强化学习（Multi-agent Reinforcement Learning for Complex Optimization）
报告摘要：For some complex domains with strategic interaction, reinforcement learning have been successfully used to learn efficient policies. This talk will discuss key techniques behind these success and their applications in domains including games, e-commerce, and urban planning.
报告人：Bo An is the President’s Council Chair Associate Professor in Computer Science and Engineering, Nanyang Technological University. He received the Ph.D degree in Computer Science from the University of Massachusetts, Amherst. His current research interests include artificial intelligence, multiagent systems, computational game theory, reinforcement learning, and optimization. His research results have been successfully applied to many domains including infrastructure security and e-commerce. He has published over 100 referred papers at AAMAS, IJCAI, AAAI, ICAPS, KDD, WWW, JAAMAS, NeurIPS, AIJ and ACM/IEEE Transactions. Dr. An was the recipient of the 2010 IFAAMAS Victor Lesser Distinguished Dissertation Award, an Operational Excellence Award from the Commander, First Coast Guard District of the United States, the 2012 INFORMS Daniel H. Wagner Prize for Excellence in Operations Research Practice, and 2018 Nanyang Research Award (Young Investigator). His publications won the Best Innovative Application Paper Award at AAMAS’12 and the Innovative Application Award at IAAI’16. He was invited to give Early Career Spotlight talk at IJCAI’17. He led the team HogRider which won the 2017 Microsoft Collaborative AI Challenge. He was named to IEEE Intelligent Systems' AI's 10 to Watch list for 2018. He was invited to be an Advisory Committee member of IJCAI’18. He is PC Co-Chair of AAMAS’20. He is a member of the editorial board of JAIR and the Associate Editor of JAAMAS, IEEE Intelligent Systems, and ACM TIST. He was elected to the board of directors of IFAAMAS and senior member of AAAI.