Distinguished Lecture Series: Feature-Based Aggregation and Deep Reinforcement Learning
Presented by Dimitri P. Bertsekas, McAfee professor of Electrical Engineering and Computer Science at Massachusetts Institute of Technology (MIT).
Professor Dimitri P. Bertsekas will provide an overview of policy iteration/self-learning methods for approximate solution of large Markov decision problems. The lecture’s focus will be on schemes that combine ideas from two major but heretofore unrelated approaches: feature-based aggregation, which has a long history in large-scale dynamic programming, and reinforcement learning based on deep neural networks, which achieved spectacular success recently in the context of games such as chess and Go.
+ Thursday, April 26, 2018, 2:30 p.m
+ College Avenue Commons (CAVC) 101, Tempe campus
+ Sponsors: School of Computing, Informatics, and Decision Systems Engineering (CIDSE) and School of Electrical, Computer, and Energy Engineering (ECEE) in the Ira A. Fulton Schools of Engineering at Arizona State University.
+ Info: CIDSE host Assistant Professor Stephanie Gil, email@example.com