Lecturer Profile |
Xianfu Chen received his Ph.D. degree in Signal and Information Processing, from the Department of Information Science and Electronic Engineering (ISEE) at Zhejiang University, Hangzhou, China, in March 2012. Since April 2012, he has been with the VTT Technical Research Centre of Finland, Oulu, Finland, where he is currently a Senior Scientist. His research interests cover various aspects of wireless communications and networking, with emphasis on human-level and artificial intelligence for resource awareness in next-generation communication networks. He is serving and served as a Track Co-Chair and a TPC member for a number of IEEE ComSoc flagship conferences. He is currently a Vice Chair of IEEE Special Interest Group on Big Data with Computational Intelligence, the members of which come from over 15 countries worldwide. |
Lecture Abstract |
As a way of achieving artificial intelligence, machine learning is capable of solving complex problems in the real world. Different from supervised learning and unsupervised learning, reinforcement learning (RL) is concerned with how an agent adjusts its actions in order to optimize an expected long-term objective by observing the feedbacks in an unknown surrounding environment. Markov decision process (MDP) provides the mathematical framework to model the interactions between the agent and the environment. In this talk, we will first briefly introduce basics of a MDP. Through several use case studies from our most recent research efforts, we will see how RL algorithms achieve the desired objectives in wireless communications only with limited feedback information received from the network. |
