University of Pittsburgh

Hacking your way to RL in the Real World (someday)

Research Scientist, Facebook AI Research; Honorary Associate Professor, UCL
Date: 
Friday, November 20, 2020 - 12:30pm - 1:30pm

Abstract:

Deep Reinforcement Learning has produced some impressive results—mostly in a particular kind of game or game-like setting—which are worthy of praise. However, we (eventually) want agents which do practical stuff in the real world. Is the real world like these games? What's the realism gap? Are there games that close it a little? All these questions will (possibly) be partially and vaguely answered as I present a new environment for RL research which will help push the boundaries of our field (without setting your cluster on fire), and discuss some recent methods developed to scratch the surface of the challenges associated with it.

 

Bio: Edward Grefenstette is a Research Scientist at Facebook AI Research, and Honorary Associate Professor at UCL. He previously was a Staff Research Scientist at DeepMind, and as a Junior Research Fellow within Oxford’s Department of Computer Science and Somerville College. He completed his DPhil (PhD) at the University of Oxford in 2013 under the supervision of Profs Coecke and Pulman, and Dr Sadrzadeh, working on applying category-theoretic tools–initially developed to model quantum information flow–to model compositionality of distributed representations in natural language semantics. His recent research has covered topics at the intersection of deep learning and machine reasoning, addressing questions such as how neural networks can model or understand logic and mathematics, infer implicit or human-readable programs, or learn to understand instructions from simulation.

Copyright 2009–2021 | Send feedback about this site