Because of the uncertainty caused by COVID-19, it is still unclear if this workshop will take place in-person or online only. Even an in-person version will have significantly reduced capacity; in-person attendance is expected to be limited to long-term program participants. In any case, the workshop will be open to the public for online participation. Please register to receive the zoom webinar access details. This page will be updated as soon as we have more information.
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.
Further details about this workshop will be posted in due course. Enquiries may be sent to the organizers workshop-rl1 [at] lists.simons.berkeley.edu (at this address).
Pieter Abbeel (UC Berkeley), Alekh Agarwal (Microsoft Research Redmond), Jacob Andreas (MIT), Michael Bowling (University of Alberta, Google DeepMind), Emma Brunskill (Stanford University), Will Dabney (DeepMind), Bo Dai (Google), Chelsea Finn (Stanford University), Matthieu Geist (Google Research), Anna Harutyunyan (DeepMind), Sham Kakade (University of Washington), Joel Lehman (Uber), Sergey Levine (UC Berkeley), Qiang Liu (UC Irvine), Tengyu Ma (Stanford University), Remi Munos (DeepMind), Ofir Nachum (Google Research), Scott Niekum (University of Texas), Ian Osband (DeepMind), Jan Peters (Technische Universitaet Darmstadt), Doina Precup (McGill University), John Schulman (OpenAI), Dale Schuurmans (University of Alberta), Martha White (University of Alberta), Cathy Wu (MIT)