Satinder Singh: The Origin Story of RLDM @ RLDM 2025

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 ReferencesRLDM 2025: Multi-disciplinary Conference on Reinforcement Learning and Decision Making (RLDM)June 11-14, 2025 at Trinity College Dublin, IrelandSatinder Singh on Google Scholar

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  

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 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