Abhishek Naik was a student at University of Alberta and Alberta Machine Intelligence Institute, and he just finished his PhD in reinforcement learning, working with Rich Sutton. Now he is a postdoc fellow at the National Research Council of Canada, where he does AI research on Space applications.

Featured References

Reinforcement Learning for Continuing Problems Using Average Reward
Abhishek Naik Ph.D. dissertation 2024

Reward Centering
Abhishek Naik, Yi Wan, Manan Tomar, Richard S. Sutton 2024

Learning and Planning in Average-Reward Markov Decision Processes
Yi Wan, Abhishek Naik, Richard S. Sutton 2020

Discounted Reinforcement Learning Is Not an Optimization Problem
Abhishek Naik, Roshan Shariff, Niko Yasui, Hengshuai Yao, Richard S. Sutton 2019

Additional References

• Explaining dopamine through prediction errors and beyond, Gershman et al 2024 (proposes Differential-TD-like learning mechanism in the brain around Box 4)

What do RL researchers complain about after hours at the bar? In this "Hot takes" episode, we find out!

Recorded at The Pearl in downtown Vancouver, during the RL meetup after a day of Neurips 2024.

Special thanks to "David Beckham" for the inspiration :)

Posters and Hallway episodes are short interviews and poster summaries. Recorded at RLC 2024 in Amherst MA.

Featuring:

• 0:01 David Radke of the Chicago Blackhawks NHL on RL for professional sports
• 0:56 Abhishek Naik from the National Research Council on Continuing RL and Average Reward
• 2:42 Daphne Cornelisse from NYU on Autonomous Driving and Multi-Agent RL
• 08:58 Shray Bansal from Georgia Tech on Cognitive Bias for Human AI Ad hoc Teamwork
• 10:21 Claas Voelcker from University of Toronto on Can we hop in general?
• 11:23 Brent Venable from The Institute for Human & Machine Cognition on Cooperative information dissemination