TalkRL podcast is All Reinforcement Learning, All the Time.
In-depth interviews with brilliant people at the forefront of RL research and practice.
Guests from places like MILA, OpenAI, MIT, DeepMind, Berkeley, Amii, Oxford, Google Research, Brown, Waymo, Caltech, and Vector Institute.
Hosted by Robin Ranjit Singh Chauhan.
Sharath Chandra Raparthy
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.
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
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
Pierluca D'Oro and Martin Klissarov
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.
Motif: 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. Bellemare
To keep doing RL research, stop calling yourself an RL researcher Pierluca D'Oro
Martin Riedmiller of Google DeepMind on controlling nuclear fusion plasma in a tokamak with RL, the original Deep Q-Network, Neural Fitted Q-Iteration, Collect and Infer, AGI for control systems, and tons more!
Martin Riedmiller is a research scientist and team lead at DeepMind.
Magnetic control of tokamak plasmas through deep reinforcement learning Jonas Degrave, Federico Felici, Jonas Buchli, Michael Neunert, Brendan Tracey, Francesco Carpanese, Timo Ewalds, Roland Hafner, Abbas Abdolmaleki, Diego de las Casas, Craig Donner, Leslie Fritz, Cristian Galperti, Andrea Huber, James Keeling, Maria Tsimpoukelli, Jackie Kay, Antoine Merle, Jean-Marc Moret, Seb Noury, Federico Pesamosca, David Pfau, Olivier Sauter, Cristian Sommariva, Stefano Coda, Basil Duval, Ambrogio Fasoli, Pushmeet Kohli, Koray Kavukcuoglu, Demis Hassabis & Martin Riedmiller
Human-level control through deep reinforcement learning Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski, Stig Petersen, Charles Beattie, Amir Sadik, Ioannis Antonoglou, Helen King, Dharshan Kumaran, Daan Wierstra, Shane Legg, Demis Hassabis
Neural fitted Q iteration–first experiences with a data efficient neural reinforcement learning method Martin Riedmiller
Max Schwarzer is a PhD student at Mila, with Aaron Courville and Marc Bellemare, interested in RL scaling, representation learning for RL, and RL for science. Max spent the last 1.5 years at Google Brain/DeepMind, and is now at Apple Machine Learning Research.
Featured References Bigger, Better, Faster: Human-level Atari with human-level efficiency Max Schwarzer, Johan Obando-Ceron, Aaron Courville, Marc Bellemare, Rishabh Agarwal, Pablo Samuel Castro Sample-Efficient Reinforcement Learning by Breaking the Replay Ratio Barrier Pierluca D'Oro, Max Schwarzer, Evgenii Nikishin, Pierre-Luc Bacon, Marc G Bellemare, Aaron Courville The Primacy Bias in Deep Reinforcement Learning Evgenii Nikishin, Max Schwarzer, Pierluca D'Oro, Pierre-Luc Bacon, Aaron Courville Additional References
Rainbow: Combining Improvements in Deep Reinforcement Learning, Hessel et al 2017 When to use parametric models in reinforcement learning? Hasselt et al 2019 Data-Efficient Reinforcement Learning with Self-Predictive Representations, Schwarzer et al 2020 Pretraining Representations for Data-Efficient Reinforcement Learning, Schwarzer et al 2021
Julian Togelius is an Associate Professor of Computer Science and Engineering at NYU, and Cofounder and research director at modl.ai
Featured References Choose Your Weapon: Survival Strategies for Depressed AI Academics
Julian Togelius, Georgios N. Yannakakis
Learning Controllable 3D Level Generators
Zehua Jiang, Sam Earle, Michael Cerny Green, Julian Togelius
PCGRL: Procedural Content Generation via Reinforcement Learning
Ahmed Khalifa, Philip Bontrager, Sam Earle, Julian Togelius
Illuminating Generalization in Deep Reinforcement Learning through Procedural Level Generation
Niels Justesen, Ruben Rodriguez Torrado, Philip Bontrager, Ahmed Khalifa, Julian Togelius, Sebastian Risi
Jakob Foerster on Multi-Agent learning, Cooperation vs Competition, Emergent Communication, Zero-shot coordination, Opponent Shaping, agents for Hanabi and Prisoner's Dilemma, and more.
Jakob Foerster is an Associate Professor at University of Oxford.
Learning with Opponent-Learning Awareness Jakob N. Foerster, Richard Y. Chen, Maruan Al-Shedivat, Shimon Whiteson, Pieter Abbeel, Igor Mordatch
Model-Free Opponent Shaping Chris Lu, Timon Willi, Christian Schroeder de Witt, Jakob Foerster
Off-Belief Learning Hengyuan Hu, Adam Lerer, Brandon Cui, David Wu, Luis Pineda, Noam Brown, Jakob Foerster Learning to Communicate with Deep Multi-Agent Reinforcement Learning Jakob N. Foerster, Yannis M. Assael, Nando de Freitas, Shimon Whiteson
Adversarial Cheap Talk Chris Lu, Timon Willi, Alistair Letcher, Jakob Foerster Cheap Talk Discovery and Utilization in Multi-Agent Reinforcement Learning Yat Long Lo, Christian Schroeder de Witt, Samuel Sokota, Jakob Nicolaus Foerster, Shimon Whiteson
Lectures by Jakob on youtube
Exposes a wide array of topics
I am a first-year PhD student and I love this podcast. It helps me to be exposed to so many idea and find fellow RL researchers. Thank you Robin for putting this together.
If you are looking for a podcast to help you advance in the field and become a better RL researcher than this is the podcast for you!5
Unexpected gem to learn deeply about RL
I had the pleasure of finding this podcast as a listener and then being on it within a month or two. Robin does a great job and is here to help improve the experience for the community.
Will help listeners rapidly get up to date with the happenings in reinforcement learning.
Love the pod cast!
Great to have a podcast that digs into the technical detail of RL!!