TalkRL: The Reinforcement Learning Podcast

David Abel, a Senior Research Scientist at DeepMind specializing in agency, dives into the fascinating intersection of reinforcement learning and philosophy. He discusses how clear definitions can drive advancements in the field and the relationship between plasticity and empowerment in intelligent agents. The conversation also covers the significance of mutual and directed information in communication theory and challenges traditional views on continual reinforcement learning, advocating for a more dynamic understanding of agency in AI.

Recorded at Reinforcement Learning Conference 2025 at University of Alberta, Edmonton Alberta Canada.Featured ReferencesLecture on the Oak Architecture, Rich SuttonAlberta Plan, Rich Sutton with Mike Bowling and Patrick Pilarski Additional ReferencesJacob Beck on Google Scholar Alex Goldie on Google ScholarCornelius Braun on Google ScholarReinforcement Learning Conference

We caught up with the RLC Outstanding Paper award winners for your listening pleasure. Recorded on location at Reinforcement Learning Conference 2025, at University of Alberta, in Edmonton Alberta Canada in August 2025.Featured References Empirical Reinforcement Learning ResearchMitigating Suboptimality of Deterministic Policy Gradients in Complex Q-functionsAyush Jain, Norio Kosaka, Xinhu Li, Kyung-Min Kim, Erdem Biyik, Joseph J LimApplications of Reinforcement LearningWOFOSTGym: A Crop Simulator for Learning Annual and Perennial Crop Management StrategiesWilliam Solow, Sandhya Saisubramanian, Alan FernEmerging Topics in Reinforcement LearningTowards Improving Reward Design in RL: A Reward Alignment Metric for RL PractitionersCalarina Muslimani, Kerrick Johnstonbaugh, Suyog Chandramouli, Serena Booth, W. Bradley Knox, Matthew E. TaylorScientific Understanding in Reinforcement LearningMulti-Task Reinforcement Learning Enables Parameter ScalingReginald McLean, Evangelos Chatzaroulas, J K Terry, Isaac Woungang, Nariman Farsad, Pablo Samuel Castro

Explore groundbreaking advancements in reinforcement learning as award-winning researchers share insights from RLC 2025. Discover innovative meta-learning techniques for algorithm discovery and the introduction of a user-friendly curriculum learning library. Dive into the power of PufferLib for ultra-efficient training in diverse applications, alongside discussions on cutting-edge algorithms like prioritized level replay in Minecraft. Lastly, engage with theories that improve convergence and performance in deep reinforcement learning.

Thomas Akam, a prominent neuroscientist from Oxford, leads the Cognitive Circuits Research Group and explores the fascinating interface of the brain and behavior. He discusses how the brain adapts actions through internal models, revealing insights into decision-making mechanisms. The conversation also highlights the metabolic costs of brain tissue, contrasting biological intelligence with AI. Additionally, Akam shares advancements in brain measurement technologies, paving the way for exciting strides in understanding cognitive processes.

Stefano V. Albrecht was previously Associate Professor at the University of Edinburgh, and is currently serving as Director of AI at startup Deepflow. He is a Program Chair of RLDM 2025 and is co-author of the MIT Press textbook "Multi-Agent Reinforcement Learning: Foundations and Modern Approaches".Featured ReferencesMulti-Agent Reinforcement Learning: Foundations and Modern ApproachesStefano V. Albrecht,  Filippos Christianos,  Lukas SchäferMIT Press, 2024RLDM 2025: Reinforcement Learning and Decision Making ConferenceDublin, IrelandEPyMARL: Extended Python MARL frameworkhttps://github.com/uoe-agents/epymarlBenchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative TasksGeorgios Papoudakis and Filippos Christianos and Lukas Schäfer and Stefano V. Albrecht

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

Discover innovative benchmarks for multi-agent reinforcement learning in wind farm control, tackling turbine performance issues. Learn about groundbreaking methods that bridge simulation and real-world applications, enhancing exploration strategies. Delve into contextual bi-level reinforcement learning, using leader-follower dynamics for optimizing rewards. Also, explore the QGEN framework, which revolutionizes queuing network simulations with deep learning, setting new standards in action space definition.

Dive into cutting-edge research from NeurIPS 2024! Explore how cultural accumulation enhances generational intelligence in reinforcement learning. Discover innovations in training device-control agents through autonomous methods, outperforming traditional techniques. Learn about improving stability and convergence in deep reinforcement learning, tackling state-action churn effectively. Finally, uncover versatile methodologies and tools that boost efficiency across various algorithms, featuring the impressive JackSmile resource.

This discussion dives into innovative methods for unsupervised skill discovery in hierarchical reinforcement learning, using driving as a practical example. It also tackles trust issues in Proximal Policy Optimization and introduces Time-Constrained Robust MDPs for improved performance. Sustainability in supercomputing is highlighted, showcasing AI's role in reducing energy consumption. Additionally, there's a focus on standardizing multi-agent reinforcement learning for better control and optimizing exploration strategies when rewards are not easily visible.