题目：Data Driven Network Control with Reinforcement Learning

报告时间：2018年12月17日（星期一）上午10点

报告地点：新科技楼1702会议室

报告人：崔曙光教授 香港中文大学（深圳）

We first start with a brief introduction of Reinforcement Learning (RL) and then discuss its applications in self-organizing networks. The first application is on handover control: We propose a two-layer framework to learn the optimal HO controllers in possibly large-scale wireless systems supporting mobile users, where the user mobility patterns could be heterogeneous. In particular, our proposed framework first partitions the User Equipments (UEs) with different mobility patterns into clusters, where the mobility patterns are similar in the same cluster. Then, within each cluster, an asynchronous multi-user deep RL scheme is developed to control the HO processes across the UEs in each cluster, in the goal of lowering the HO rate while ensuring certain system throughput. At each user, a deep-RL framework with LSTM RNN is used. We show that the adopted global-parameter-based asynchronous framework enables us to train faster with more UEs, which could nicely address the scalability issue to support large systems. The second application is on joint energy and access control in energy harvesting wireless systems, where we show that a double-deep-RL solution could lead us to significant system gains.

崔曙光教授：2005年获得斯坦福大学的电子工程专业博士学位，现担任美国德克萨斯农工大学电气与计算机工程系的教授职位。崔曙光是网络信息处理，特别是传感器网络和物联网领域权威学者，进入汤森路透全球“高引用科学家”名单。现任深圳市大数据研究院副经理和香港中文大学（深圳）董事长讲席教授。

崔曙光的研究论文被广泛地引用，在2014年当选为Thomson Reuters高被引科学家，并被Sciencewatch列为世界最具影响力科学家之一。他荣获了IEEE Signal Processing Society 2012最佳论文奖，也是两次最佳会议论文的获得者。他一直在担任多个专业会议、期刊和委员会的主席、分区主编或副主编。他在2013年当选了IEEE Fellow，并在2014年当选IEEE通信协会杰出讲师。2018年入选教育部长江学者和广东省珠江创新团队带头人。