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

  
  

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

Featuring:

• 0:01 David Abel from DeepMind on 3 Dogmas of RL
• 0:55 Kevin Wang from Brown on learning variable depth search for MCTS
• 2:17 Ashwin Kumar from Washington University in St Louis on fairness in resource allocation
• 3:36 Prabhat Nagarajan from UAlberta on Value overestimation

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

Featuring:

• 0:01 Kris De Asis from Openmind on Time Discretization
• 2:23 Anna Hakhverdyan from U of Alberta on Online Hyperparameters
• 3:59 Dilip Arumugam from Princeton on Information Theory and Exploration
• 5:04 Micah Carroll from UC Berkeley on Changing preferences and AI alignment

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

Featuring:

• 0:01 Hector Kohler from Centre Inria de l'Université de Lille with "Interpretable and Editable Programmatic Tree Policies for Reinforcement Learning"
• 2:29 Quentin Delfosse from TU Darmstadt on "Interpretable Concept Bottlenecks to Align Reinforcement Learning Agents"
• 4:15 Sonja Johnson-Yu from Harvard on "Understanding biological active sensing behaviors by interpreting learned artificial agent policies"
• 6:42 Jannis Blüml from TU Darmstadt on "OCAtari: Object-Centric Atari 2600 Reinforcement Learning Environments"
• 8:20 Cameron Allen from UC Berkeley on "Resolving Partial Observability in Decision Processes via the Lambda Discrepancy"
• 9:48 James Staley from Tufts on "Agent-Centric Human Demonstrations Train World Models"
• 14:54 Jonathan Li from Rensselaer Polytechnic Institute

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

Featuring:

• 0:01 Ann Huang from Harvard on Learning Dynamics and the Geometry of Neural Dynamics in Recurrent Neural Controllers
• 1:37 Jannis Blüml from TU Darmstadt on HackAtari: Atari Learning Environments for Robust and Continual Reinforcement Learning
• 3:13 Benjamin Fuhrer from NVIDIA on Gradient Boosting Reinforcement Learning
• 3:54 Paul Festor from Imperial College London on Evaluating the impact of explainable RL on physician decision-making in high-fidelity simulations: insights from eye-tracking metrics

Finale Doshi-Velez is a Professor at the Harvard Paulson School of Engineering and Applied Sciences.

This off-the-cuff interview was recorded at UMass Amherst during the workshop day of RL Conference on August 9th 2024.

Host notes: I've been a fan of some of Prof Doshi-Velez' past work on clinical RL and hoped to feature her for some time now, so I jumped at the chance to get a few minutes of her thoughts -- even though you can tell I was not prepared and a bit flustered tbh. Thanks to Prof Doshi-Velez for taking a moment for this, and I hope to cross paths in future for a more in depth interview.

References

• Finale Doshi-Velez Homepage @ Harvard
• Finale Doshi-Velez on Google Scholar