The success of deep neural networks in modeling complicated functions has recently been applied by the reinforcement learning community, resulting in algorithms that are able to learn in environments previously thought to be much too large. Successful applications span domains from robotics to health care. However, the success is not well understood from a theoretical perspective. What are the modeling choices necessary for good performance, and how does the flexibility of deep neural nets help learning? This workshop will connect practitioners to theoreticians with the goal of understanding the most impactful modeling decisions and the properties of deep neural networks that make them so successful. Specifically, we will study the ability of deep neural nets to approximate in the context of reinforcement learning.
If you require accommodation for communication, information about mobility access, or have dietary restrictions, please contact our Access Coordinator at simonsevents [at] berkeley.edu (subject: Workshop%20accessibility) with as much advance notice as possible.
Pieter Abbeel (UC Berkeley), Rediet Abebe (Harvard), Alekh Agarwal (Microsoft Research Redmond), Jacob Andreas (MIT), Luca Baldassarre (Swiss Re), Jalaj Bhandari (Columbia University), Jeffrey Bohn (Swiss Re), Vivek Shripad Borkar (Indian Institute of Technology Bombay), Michael Bowling (University of Alberta, Google DeepMind), Emma Brunskill (Stanford University), Sebastien Bubeck (Microsoft Research), Shantanu Prasad Burnwal (IIT Hyderabad), Marco Campi (University of Brescia), Rene Carmona (Princeton University), Lin Chen (Yale University), Brian Christian (UC Berkeley), Bo Dai (Google), Chelsea Finn (Stanford University), Dylan Foster (Massachusetts Institute of Technology (MIT)), Germano Gabbianelli (Universitat Pompeu Fabra), Matthieu Geist (Google Research), Anupam Gupta (Carnegie Mellon University), Nika Haghtalab (Cornell University), Anna Harutyunyan (DeepMind), Niao He (University of Illinois at Urbana-Champaign), Rahul Jain (University of Southern California), Chi Jin (Princeton University), Mihailo Jovanovic (University of Southern California), Sham Kakade (University of Washington), Ravindran Kannan (Microsoft Research India), Mikhail Konobeev (University of Alberta), Wouter Koolen (Centrum Wiskunde & Informatica), Akshay Krishnamurthy (Microsoft Research), Jason Lee (Princeton University), Sergey Levine (UC Berkeley), Lihong Li (Google Brain), Yao Liu (Stanford), Qiang Liu (UC Irvine), Tengyu Ma (Stanford University), Sean Meyn (University of Florida), Aditya Modi (University of Michigan, Ann Arbor), Eric Moulines (Ecole Polytechnique), Remi Munos (DeepMind), Vidya Muthukumar (UC Berkeley), Ofir Nachum (Google Research), Raju Nair (Swiss Re), Joseph Naor (Technion - Israel Institute of Technology), Angelia Nedich (Arizona State University), Gergely Neu (UPF), Scott Niekum (University of Texas), Ian Osband (DeepMind), Ashwin Pananjady (UC Berkeley), Jan Peters (Technische Universitaet Darmstadt), Marek Petrik (University of New Hampshire), Doina Precup (McGill University), Balaraman Ravindran (IIT Madras), Daniel Russo (Columbia University), Barna Saha (UC Berkeley), Sergey Samsonov (National Research University Higher School of Economics), Bruno Scherrer (INRIA), John Schulman (OpenAI), Dale Schuurmans (University of Alberta), Roshan Shariff (University of Alberta), Mohamad Kazem Shirani Faradonbeh (University of Florida), Aaron Sidford (Stanford University), Sean Sinclair (Cornell University), Phoebe Sun (Swiss Re), Csaba Szepesvári (University of Alberta, Google DeepMind), Ambuj Tewari (University of Michigan), Claire Tomlin (UC Berkeley), Mathukumalli Vidyasagar (IIT Hyderabad), Stefan Wager (Stanford Graduate School of Business), Martin Wainwright (UC Berkeley), Zhaoran Wang (Northwestern University), Guan Wang (Swiss Re), Mengdi Wang (Princeton University), Chen-Yu Wei (University of Southern California), Martha White (University of Alberta), Cathy Wu (MIT), Boyi Xie (Swiss Re), Lin Yang (University of California, Los Angeles), Zhuoran Yang (Princeton University), Christina Yu (Cornell University), Huizhen Yu (University of Alberta), Andrea Zanette (Stanford University)