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Professor Satinder Singh of Google DeepMind and U of Michigan is co-founder of RLDM. Here he narrates the origin story of the Reinforcement Learning and Decision Making meeting (not conference).
Recorded on location at Trinity College Dublin, Ireland during RLDM 2025.
Featured References
RLDM 2025: Multi-disciplinary Conference on Reinforcement Learning and Decision Making (RLDM)
June 11-14, 2025 at Trinity College Dublin, IrelandSatinder Singh on Google Scholar
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Posters and Hallway episodes are short interviews and poster summaries. Recorded at NeurIPS 2024 in Vancouver BC Canada.
Featuring
Claire Bizon Monroc from Inria: WFCRL: A Multi-Agent Reinforcement Learning Benchmark for Wind Farm Control Andrew Wagenmaker from UC Berkeley: Overcoming the Sim-to-Real Gap: Leveraging Simulation to Learn to Explore for Real-World RL Harley Wiltzer from MILA: Foundations of Multivariate Distributional Reinforcement Learning Vinzenz Thoma from ETH AI Center: Contextual Bilevel Reinforcement Learning for Incentive Alignment Haozhe (Tony) Chen & Ang (Leon) Li from Columbia: QGym: Scalable Simulation and Benchmarking of Queuing Network Controllers -
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Posters and Hallway episodes are short interviews and poster summaries. Recorded at NeurIPS 2024 in Vancouver BC Canada.
Featuring
Jonathan Cook from University of Oxford: Artificial Generational Intelligence: Cultural Accumulation in Reinforcement Learning Yifei Zhou from Berkeley AI Research: DigiRL: Training In-The-Wild Device-Control Agents with Autonomous Reinforcement Learning Rory Young from University of Glasgow: Enhancing Robustness in Deep Reinforcement Learning: A Lyapunov Exponent Approach Glen Berseth from MILA: Improving Deep Reinforcement Learning by Reducing the Chain Effect of Value and Policy Churn Alexander Rutherford from University of Oxford: JaxMARL: Multi-Agent RL Environments and Algorithms in JAX -
Posters and Hallway episodes are short interviews and poster summaries. Recorded at NeurIPS 2024 in Vancouver BC Canada.
Featuring
Jiaheng Hu of University of Texas: Disentangled Unsupervised Skill Discovery for Efficient Hierarchical Reinforcement Learning Skander Moalla of EPFL: No Representation, No Trust: Connecting Representation, Collapse, and Trust Issues in PPO Adil Zouitine of IRT Saint Exupery/Hugging Face : Time-Constrained Robust MDPs Soumyendu Sarkar of HP Labs : SustainDC: Benchmarking for Sustainable Data Center Control Matteo Bettini of Cambridge University: BenchMARL: Benchmarking Multi-Agent Reinforcement Learning Michael Bowling of U Alberta : Beyond Optimism: Exploration With Partially Observable Rewards -
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 2024Reward Centering
Abhishek Naik, Yi Wan, Manan Tomar, Richard S. Sutton 2024Learning and Planning in Average-Reward Markov Decision Processes
Yi Wan, Abhishek Naik, Richard S. Sutton 2020Discounted Reinforcement Learning Is Not an Optimization Problem
Abhishek Naik, Roshan Shariff, Niko Yasui, Hengshuai Yao, Richard S. Sutton 2019
Explaining dopamine through prediction errors and beyond, Gershman et al 2024 (proposes Differential-TD-like learning mechanism in the brain around Box 4)
Additional References -
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 :)
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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.
AlphaProof announcement on DeepMind's blogDiscovering 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
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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
TorchRL on github TensorDict Documentation
Additional References -
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 1992Learning from Delayed Rewards, Chris Watkins 1989Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning, Williams 1992 -
Glen Berseth is an assistant professor at the Université de Montréal, a core academic member of the Mila - Quebec AI Institute, a Canada CIFAR AI chair, member l'Institute Courtios, and co-director of the Robotics and Embodied AI Lab (REAL).
Featured Links
Reinforcement Learning Conference
Closing the Gap between TD Learning and Supervised Learning--A Generalisation Point of View
Raj Ghugare, Matthieu Geist, Glen Berseth, Benjamin Eysenbach -
Ian Osband is a Research scientist at OpenAI (ex DeepMind, Stanford) working on decision making under uncertainty.
We spoke about:
- Information theory and RL
- Exploration, epistemic uncertainty and joint predictions
- Epistemic Neural Networks and scaling to LLMs
Featured ReferencesReinforcement Learning, Bit by Bit
Xiuyuan Lu, Benjamin Van Roy, Vikranth Dwaracherla, Morteza Ibrahimi, Ian Osband, Zheng WenFrom Predictions to Decisions: The Importance of Joint Predictive Distributions
Zheng Wen, Ian Osband, Chao Qin, Xiuyuan Lu, Morteza Ibrahimi, Vikranth Dwaracherla, Mohammad Asghari, Benjamin Van Roy
Epistemic Neural Networks
Ian Osband, Zheng Wen, Seyed Mohammad Asghari, Vikranth Dwaracherla, Morteza Ibrahimi, Xiuyuan Lu, Benjamin Van Roy
Approximate Thompson Sampling via Epistemic Neural NetworksIan Osband, Zheng Wen, Seyed Mohammad Asghari, Vikranth Dwaracherla, Morteza Ibrahimi, Xiuyuan Lu, Benjamin Van Roy
Thesis defence, Ian Osband Homepage, Ian Osband Epistemic Neural Networks at Stanford RL Forum Behaviour Suite for Reinforcement Learning, Osband et al 2019 Efficient Exploration for LLMs, Dwaracherla et al 2024
Additional References -
Sharath Chandra Raparthy on In-Context Learning for Sequential Decision Tasks, GFlowNets, and more!
Sharath Chandra Raparthy is an AI Resident at FAIR at Meta, and did his Master's at Mila.
Sharath Chandra Raparthy Homepage Human-Timescale Adaptation in an Open-Ended Task Space, Adaptive Agent Team 2023Data Distributional Properties Drive Emergent In-Context Learning in Transformers, Chan et al 2022 Decision Transformer: Reinforcement Learning via Sequence Modeling, Chen et al 2021
Featured Reference
Generalization to New Sequential Decision Making Tasks with In-Context Learning
Sharath Chandra Raparthy , Eric Hambro, Robert Kirk , Mikael Henaff, , Roberta Raileanu
Additional References -
Pierluca D'Oro and Martin Klissarov on Motif and RLAIF, Noisy Neighborhoods and Return Landscapes, and more!
Pierluca D'Oro is PhD student at Mila and visiting researcher at Meta.
Martin Klissarov is a PhD student at Mila and McGill and research scientist intern at Meta.
Featured ReferencesMotif: Intrinsic Motivation from Artificial Intelligence Feedback
Martin Klissarov*, Pierluca D'Oro*, Shagun Sodhani, Roberta Raileanu, Pierre-Luc Bacon, Pascal Vincent, Amy Zhang, Mikael Henaff
Policy Optimization in a Noisy Neighborhood: On Return Landscapes in Continuous Control
Nate Rahn*, Pierluca D'Oro*, Harley Wiltzer, Pierre-Luc Bacon, Marc G. BellemareTo keep doing RL research, stop calling yourself an RL researcher
Pierluca D'Oro - もっと表示する
