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X-WR-CALNAME:Department of Electrical and Computer Engineering (HKUECE) 電機與計算機工程系
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X-WR-CALDESC:Events for Department of Electrical and Computer Engineering (HKUECE) 電機與計算機工程系
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DTSTART:20230101T000000
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DTSTART;TZID=Asia/Hong_Kong:20240716T140000
DTEND;TZID=Asia/Hong_Kong:20240716T160000
DTSTAMP:20260512T160728
CREATED:20240702T075019Z
LAST-MODIFIED:20250114T043117Z
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SUMMARY:Introduction to Reinforcement Learning
DESCRIPTION:Abstract\nThis seminar aims to introduce reinforcement learning (RL) and its application to communication systems to graduate students\, although everyone is welcome to attend. RL has successfully been applied to many application domains ranging from control of communication and computer systems\, navigation of driverless vehicles\, robots and flying drones\, to guiding medical imaging and surgery\, to name a few. The speaker will first present the Markov Decision Process (MDP) – the mathematical foundation of RL. To solve the MDP\, the goal is to derive the optimal (action) policy that decides the optimal action for every given state of the system to maximize the long-term reward. As the underlying models for many application settings are unknown\, various model-free RL techniques have been developed\, including temporal difference learning\, SARSA\, Q-learning and policy gradient methods. The speaker will briefly describe these techniques. Furthermore\, as the system complexity increases\, neural networks are used to approximate the Q-values (rewards) and/or action policies as functions of system states and actions. This has led to deep RL where the neural-network parameters are “trained” or “learned” from processing the observed data from practical systems. For illustration purposes\, deep RL is used to manage communication infrastructures. New techniques will be highlighted to overcome issues of model complexity and long training time.  Open research issues on RL will also be briefly discussed. \nSpeaker\nProf. Kin K. LEUNG\nTanaka Chair Professor\,\nElectrical and Electronic Engineering\, and Computing Departments\,\nImperial College\, London \nBiography of the Speaker\nProf. Kin K. LEUNG received his B.S. degree from the Chinese University of Hong Kong\, and his M.S. and Ph.D. degrees from University of California\, Los Angeles. He worked at AT&T Bell Labs and its successor companies in New Jersey from 1986 to 2004. Since then\, he has been the Tanaka Chair Professor in the Electrical and Electronic Engineering (EEE)\, and Computing Departments at Imperial College in London. He also served as the Head of Communications and Signal Processing Group in the EEE Department at Imperial from 2009 to 2024. His current research focuses on optimization and machine learning for system design and control of large-scale communications\, computer and quantum networks. He also works on multi-antenna and cross-layer designs for wireless networks. \nHe is a Fellow of the Royal Academy of Engineering\, IEEE Fellow\, IET Fellow\, and member of Academia Europaea. He received the Distinguished Member of Technical Staff Award from AT&T Bell Labs and the Royal Society Wolfson Research Merits Award. Jointly with his collaborators\, he received the IEEE Communications Society (ComSoc) Leonard G. Abraham Prize (2021)\, the IEEE ComSoc Best Survey Paper Award (2022)\, the U.S.–UK Science and Technology Stocktake Award (2021)\, the Lanchester Prize Honorable Mention Award (1997)\, and several best conference paper awards. He was an IEEE ComSoc Distinguished Lecturer. In 2012-15\, he chaired the IEEE Fellow Evaluation Committee for ComSoc. He has served as an editor for 10 IEEE and ACM journals and chaired the Steering Committee for the IEEE Transactions on Mobile Computing. Currently\, he is an editor for the ACM Computing Survey and International Journal of Sensor Networks. \nAll are welcome! We look forward to seeing you!
URL:https://ece.hku.hk/events/20240716-1/
LOCATION:Room CB-603\, 6/F\, Chow Yei Ching Building\, The University of Hong Kong
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2024/07/1280-4.jpg
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