题    目：Deep Reinforcement Learning for Complex Decision-Making

主讲人：Mingfei Sun 教授

单    位：University of Manchester

时    间：2025年7月7日 10:30

地    点：九章学堂南楼C座209

摘    要：Deep reinforcement learning proves to be an effective optimization method for resolving numerous complex decision-making problems. In this seminar talk, I will begin by providing a brief overview of deep reinforcement learning and the underlying model, Markov Decision Processes (MDP). Subsequently, I will delve into the challenges that make MDP hard to solve, addressing key issues such as the extremely vast state/action space, the reward alignment problem, and the policy generalization challenge. Furthermore, I will showcase examples of employing deep reinforcement learning to tackle challenging problems in video games, multiagent systems, and simulated robot controls. Towards the conclusion of the talk, I will introduce the ongoing challenges we are currently addressing, with a specific emphasis on combinatorial decision-making on graphs and efficient optimization methods for large models.

简    介：Dr. Mingfei Sun is an Assistant Professor in Machine Learning in the Department of Computer Science and leads the Agent Lab at the University of Manchester (UoM). His research focuses on deep reinforcement learning and deep learning for decision-making. Dr. Sun serves on the Admission Panel for the AI Centres for Doctoral Training Programme in Complex Decision-Making and holds the position of Student Experience Director. Prior to joining UoM, Dr. Sun worked as a researcher at Microsoft Research Cambridge, focusing on foundation models, and as a research associate at the University of Oxford, specializing in multi-agent systems. Dr. Sun's work has been recognized at leading machine learning and artificial intelligence conferences, including NeurIPS, ICLR, KDD, AAAI, IJCAI, and AAMAS. He has received prestigious honors such as the Best Paper Award at AAMAS and the Google Summer of Code Award.（邀请人：杨翔宇）