TalkRL: The Reinforcement Learning Podcast

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  

Thanks to Professor Silver for permission to record this discussion after his RLC 2024 keynote lecture.   

Recorded at UMass Amherst during RCL 2024.

Due to the live recording environment, audio quality varies.  We publish this audio in its raw form to preserve the authenticity and immediacy of the discussion.   

References  

• AlphaProof announcement on DeepMind's blog
• Discovering Reinforcement Learning Algorithms, Oh et al  -- His keynote at RLC 2024 referred to more recent update to this work, yet to be published  
• Reinforcement Learning Conference 2024  
• David Silver on Google Scholar  

David Silver is a principal research scientist at DeepMind and a professor at University College London. 

This interview was recorded at UMass Amherst during RLC 2024.   

References  

• Discovering Reinforcement Learning Algorithms, Oh et al  -- His keynote at RLC 2024 referred to more recent update to this work, yet to be published  
• Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm, Silver et al 2017 -- the AlphaZero algo was used   in his recent work on AlphaProof  
• AlphaProof on the DeepMind blog 
• AlphaFold on the DeepMind blog 
• Reinforcement Learning Conference 2024  
• David Silver on Google Scholar  

Dr. Vincent Moens is an Applied Machine Learning Research Scientist at Meta, and an author of TorchRL and TensorDict in pytorch. 

Featured References

TorchRL: A data-driven decision-making library for PyTorch
Albert Bou, Matteo Bettini, Sebastian Dittert, Vikash Kumar, Shagun Sodhani, Xiaomeng Yang, Gianni De Fabritiis, Vincent Moens 

Additional References  

• TorchRL on github  
• TensorDict Documentation  

Arash Ahmadian is a Researcher at Cohere and Cohere For AI focussed on Preference Training of large language models. He’s also a researcher at the Vector Institute of AI.

Featured Reference

Back to Basics: Revisiting REINFORCE Style Optimization for Learning from Human Feedback in LLMs

Arash Ahmadian, Chris Cremer, Matthias Gallé, Marzieh Fadaee, Julia Kreutzer, Olivier Pietquin, Ahmet Üstün, Sara Hooker

Additional References

• Self-Rewarding Language Models, Yuan et al 2024
• Reinforcement Learning: An Introduction, Sutton and Barto 1992
• Learning from Delayed Rewards, Chris Watkins 1989
• Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning, Williams 1992