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【暑期短课】A Tutorial on Reinforcement Learning - Prof. Benjamin Van Roy -香港快三-首页

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          【暑期短课】A Tutorial on Reinforcement Learning - Prof. Benjamin Van Roy

          • 2019.07.15
          • 活動
          A Tutorial on Reinforcement Learning

          主题: A Tutorial on Reinforcement Learning

          报告人: Prof. Benjamin Van Roy, Stanford University

          时间: 10:00 am - 11:30 am, July 15 and July 17, 2019

          地点: Room 201, Teaching?Building B (July 15)

          ? ? ? ? ? Room 208, Cheng Dao Building (July 17)

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          摘要:

          There is sometimes confusion about what reinforcement learning is about. This is partly because the term alternately refers to a problem, a community who work on the problem, and methods developed by this community, some of which have been useful in addressing other problems. The reinforcement learning problem is that faced by an agent interacting with an uncertain environment aiming to maximize rewards it accumulates over time. This tutorial will introduce the problem and basic policy and value function learning algorithms that aim to address it. We will also discuss data efficiency and the role of exploration, generalized value functions, and hierarchical reinforcement learning.

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          簡介:

          Benjamin Van Roy is a Professor at Stanford University, where he has served on the faculty since 1998. His research focuses on understanding how an agent interacting with a poorly understood environment can learn over time to make effective decisions. He is interested in the design of efficient reinforcement learning algorithms, understanding what is possible or impossible in this domain, and applying the technology toward the benefit of society. Beyond academia, he leads a DeepMind Research team in Mountain View, and has also led research programs at Unica (acquired by IBM), Enuvis (acquired by SiRF), and Morgan Stanley.?

          He is a Fellow of INFORMS and IEEE and has served on the editorial boards of Machine Learning, Mathematics of Operations Research, for which he co-edits the Learning Theory Area, Operations Research, for which he edited the Financial Engineering Area, and the INFORMS Journal on Optimization.

          He received the SB in Computer Science and Engineering and the SM and PhD in Electrical Engineering and Computer Science, all from MIT. He has been a recipient of the MIT George C. Newton Undergraduate Laboratory Project Award, the MIT Morris J. Levin Memorial Master's Thesis Award, the MIT George M. Sprowls Doctoral Dissertation Award, the National Science Foundation CAREER Award, the Stanford Tau Beta Pi Award for Excellence in Undergraduate Teaching, and the Management Science and Engineering Department's Graduate Teaching Award. He has held visiting positions as the Wolfgang and Helga Gaul Visiting Professor at the University of Karlsruhe, the Chin Sophonpanich Foundation Professor and the InTouch Professor at Chulalongkorn University, a Visiting Professor at the National University of Singapore, and a Visiting Professor at the Chinese University of Hong Kong, Shenzhen.

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