Deep Reinforcement Learning: Recent Advances and Frontiers
Deep reinforcement learning has enabled artificial agents to achieve human-level performances across many challenging domains, e.g. playing Atari games and Go. I will present several important algorithms including deep Q-Networks and asynchronous actor-critic algorithms (A3C). I will discuss major challenges and promising results towards making deep reinforcement learning applicable to real world problems in robotics and natural language processing.
Li Erran Li received his Ph.D. in Computer Science from Cornell University advised by Joseph Halpern. He is currently with Uber and an adjunct professor in the Computer Science Department of Columbia University. Before that, He worked as a researcher in Bell Labs. His research interests are AI, machine learning algorithms and systems. He is an IEEE Fellow and an ACM Distinguished Scientist.