Abstract:
Much of the excitement in AI is due to recent advances in Deep Reinforcement Learning (DRL). By using DRL to overcome the curse of dimensionality in dynamic programming, researchers have recently made surprising breakthroughs in learning to play Go and the Atari video games, and learning to control high-dimensional robotic locomotion. In this talk, we will review some of the major developments in DRL since 2015, including AlphaZero, Deep Q-Learning, and actor-critic algorithms. We will also briefly describe some of our own recent work in the area. 

Bio:
Keith Ross is the Dean of Engineering and Computer Science at NYU Shanghai. He is also the co-director of the Center of Data Science and Artificial Intelligence at NYU Shanghai. Previously he was a full professor at University of Pennsylvania, Eurecom Institute, Polytechnic Institute, and NYU. He received a Ph.D. in Computer and Control Engineering from The University of Michigan. He is the recipient of several prestigious best paper awards, and his work has been featured in the mainstream press. He is the co-author of the most popular textbook on computer networking, which has been translated into 13 languages. He is an ACM Fellow and an IEEE Fellow.
His current research interests are in reinforcement learning. He has also worked in Internet privacy, peer-to-peer networking, Internet measurement, stochastic modeling of computer networks, queuing theory, and Markov decision processes. At NYU Shanghai he has been teaching Machine Learning, Reinforcement Learning, and Introduction to Computer Programming.

* The colloquium is co-hosted by DKU Colloquium and Data Science Research Center