TalkRL: The Reinforcement Learning Podcast

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.  

Featured References  

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  

Additional References  

• Lectures by Jakob on youtube 

Danijar Hafner on the DreamerV3 agent and world models, the Director agent and heirarchical RL,  realtime RL on robots with DayDreamer, and his framework for unsupervised agent design!

Danijar Hafner is a PhD candidate at the University of Toronto with Jimmy Ba, a visiting student at UC Berkeley with Pieter Abbeel, and an intern at DeepMind.  He has been our guest before back on episode 11.  

Featured References   

Mastering Diverse Domains through World Models [ blog ] DreaverV3 

Danijar Hafner, Jurgis Pasukonis, Jimmy Ba, Timothy Lillicrap  

DayDreamer: World Models for Physical Robot Learning [ blog ] 
Philipp Wu, Alejandro Escontrela, Danijar Hafner, Ken Goldberg, Pieter Abbeel 

Deep Hierarchical Planning from Pixels [ blog ] 
Danijar Hafner, Kuang-Huei Lee, Ian Fischer, Pieter Abbeel   

Action and Perception as Divergence Minimization [ blog ] 
Danijar Hafner, Pedro A. Ortega, Jimmy Ba, Thomas Parr, Karl Friston, Nicolas Heess 

Additional References  

• Mastering Atari with Discrete World Models [ blog ] DreaverV2 ; Danijar Hafner, Timothy Lillicrap, Mohammad Norouzi, Jimmy Ba  
• Dream to Control: Learning Behaviors by Latent Imagination [ blog ] Dreamer ; Danijar Hafner, Timothy Lillicrap, Jimmy Ba, Mohammad Norouzi  
• Planning to Explore via Self-Supervised World Models ; Ramanan Sekar, Oleh Rybkin, Kostas Daniilidis, Pieter Abbeel, Danijar Hafner, Deepak Pathak  

AI Generating Algos, Learning to play Minecraft with Video PreTraining (VPT), Go-Explore for hard exploration, POET and Open Endedness, AI-GAs and ChatGPT, AGI predictions, and lots more!  

Professor Jeff Clune is Associate Professor of Computer Science at University of British Columbia, a Canada CIFAR AI Chair and Faculty Member at Vector Institute, and Senior Research Advisor at DeepMind.  

Featured References 

Video PreTraining (VPT): Learning to Act by Watching Unlabeled Online Videos [ Blog Post ]
Bowen Baker, Ilge Akkaya, Peter Zhokhov, Joost Huizinga, Jie Tang, Adrien Ecoffet, Brandon Houghton, Raul Sampedro, Jeff Clune 

Robots that can adapt like animals
Antoine Cully, Jeff Clune, Danesh Tarapore, Jean-Baptiste Mouret 

Illuminating search spaces by mapping elites
Jean-Baptiste Mouret, Jeff Clune 

Enhanced POET: Open-Ended Reinforcement Learning through Unbounded Invention of Learning Challenges and their Solutions
Rui Wang, Joel Lehman, Aditya Rawal, Jiale Zhi, Yulun Li, Jeff Clune, Kenneth O. Stanley 

Paired Open-Ended Trailblazer (POET): Endlessly Generating Increasingly Complex and Diverse Learning Environments and Their Solutions
Rui Wang, Joel Lehman, Jeff Clune, Kenneth O. Stanley 

First return, then explore
Adrien Ecoffet, Joost Huizinga, Joel Lehman, Kenneth O. Stanley, Jeff Clune

Hear about why OpenAI cites her work in RLHF and dialog models, approaches to rewards in RLHF, ChatGPT, Industry vs Academia, PsiPhi-Learning, AGI and more! 

Dr Natasha Jaques is a Senior Research Scientist at Google Brain.

Featured References

Way Off-Policy Batch Deep Reinforcement Learning of Implicit Human Preferences in Dialog
Natasha Jaques, Asma Ghandeharioun, Judy Hanwen Shen, Craig Ferguson, Agata Lapedriza, Noah Jones, Shixiang Gu, Rosalind Picard 

Sequence Tutor: Conservative Fine-Tuning of Sequence Generation Models with KL-control
Natasha Jaques, Shixiang Gu, Dzmitry Bahdanau, José Miguel Hernández-Lobato, Richard E. Turner, Douglas Eck 

PsiPhi-Learning: Reinforcement Learning with Demonstrations using Successor Features and Inverse Temporal Difference Learning
Angelos Filos, Clare Lyle, Yarin Gal, Sergey Levine, Natasha Jaques, Gregory Farquhar 

Basis for Intentions: Efficient Inverse Reinforcement Learning using Past Experience
Marwa Abdulhai, Natasha Jaques, Sergey Levine 

Additional References  

• Fine-Tuning Language Models from Human Preferences, Daniel M. Ziegler et al 2019  
• Learning to summarize from human feedback, Nisan Stiennon et al 2020  
• Training language models to follow instructions with human feedback, Long Ouyang et al 2022  

Jacob Beck and Risto Vuorio on their recent Survey of Meta-Reinforcement Learning.  Jacob and Risto are Ph.D. students at Whiteson Research Lab at University of Oxford.   

Featured Reference   

A Survey of Meta-Reinforcement Learning
Jacob Beck, Risto Vuorio, Evan Zheran Liu, Zheng Xiong, Luisa Zintgraf, Chelsea Finn, Shimon Whiteson   

Additional References  

• VariBAD: A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning, Luisa Zintgraf et al  
• Mastering Diverse Domains through World Models (Dreamerv3), Hafner et al    
• Unsupervised Meta-Learning for Reinforcement Learning (MAML), Gupta et al  
• Decoupling Exploration and Exploitation for Meta-Reinforcement Learning without Sacrifices (DREAM), Liu et al  
• RL2: Fast Reinforcement Learning via Slow Reinforcement Learning, Duan et al  
• Learning to reinforcement learn, Wang et al  

John Schulman is a cofounder of OpenAI, and currently a researcher and engineer at OpenAI.

Featured References

WebGPT: Browser-assisted question-answering with human feedback
Reiichiro Nakano, Jacob Hilton, Suchir Balaji, Jeff Wu, Long Ouyang, Christina Kim, Christopher Hesse, Shantanu Jain, Vineet Kosaraju, William Saunders, Xu Jiang, Karl Cobbe, Tyna Eloundou, Gretchen Krueger, Kevin Button, Matthew Knight, Benjamin Chess, John Schulman

Training language models to follow instructions with human feedback
Long Ouyang, Jeff Wu, Xu Jiang, Diogo Almeida, Carroll L. Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul Christiano, Jan Leike, Ryan Lowe

Additional References

• Our approach to alignment research, OpenAI 2022
• Training Verifiers to Solve Math Word Problems, Cobbe et al 2021
• UC Berkeley Deep RL Bootcamp Lecture 6: Nuts and Bolts of Deep RL Experimentation, John Schulman 2017
• Proximal Policy Optimization Algorithms, Schulman 2017
• Optimizing Expectations: From Deep Reinforcement Learning to Stochastic Computation Graphs, Schulman 2016

Sven Mika is the Reinforcement Learning Team Lead at Anyscale, and lead committer of RLlib. He holds a PhD in biomathematics, bioinformatics, and computational biology from Witten/Herdecke University. 

Featured References

RLlib Documentation: RLlib: Industry-Grade Reinforcement Learning

Ray: Documentation

RLlib: Abstractions for Distributed Reinforcement Learning
Eric Liang, Richard Liaw, Philipp Moritz, Robert Nishihara, Roy Fox, Ken Goldberg, Joseph E. Gonzalez, Michael I. Jordan, Ion Stoica

Episode sponsor: Anyscale

Ray Summit 2022 is coming to San Francisco on August 23-24.
Hear how teams at Dow, Verizon, Riot Games, and more are solving their RL challenges with Ray's RLlib.

Register at raysummit.org and use code RAYSUMMIT22RL for a further 25% off the already reduced prices.

Karol Hausman is a Senior Research Scientist at Google Brain and an Adjunct Professor at Stanford working on robotics and machine learning. Karol is interested in enabling robots to acquire general-purpose skills with minimal supervision in real-world environments.

Fei Xia is a Research Scientist with Google Research. Fei Xia is mostly interested in robot learning in complex and unstructured environments. Previously he has been approaching this problem by learning in realistic and scalable simulation environments (GibsonEnv, iGibson). Most recently, he has been exploring using foundation models for those challenges.

Featured References

Do As I Can, Not As I Say: Grounding Language in Robotic Affordances [ website ]
Michael Ahn, Anthony Brohan, Noah Brown, Yevgen Chebotar, Omar Cortes, Byron David, Chelsea Finn, Keerthana Gopalakrishnan, Karol Hausman, Alex Herzog, Daniel Ho, Jasmine Hsu, Julian Ibarz, Brian Ichter, Alex Irpan, Eric Jang, Rosario Jauregui Ruano, Kyle Jeffrey, Sally Jesmonth, Nikhil J Joshi, Ryan Julian, Dmitry Kalashnikov, Yuheng Kuang, Kuang-Huei Lee, Sergey Levine, Yao Lu, Linda Luu, Carolina Parada, Peter Pastor, Jornell Quiambao, Kanishka Rao, Jarek Rettinghouse, Diego Reyes, Pierre Sermanet, Nicolas Sievers, Clayton Tan, Alexander Toshev, Vincent Vanhoucke, Fei Xia, Ted Xiao, Peng Xu, Sichun Xu, Mengyuan Yan

Inner Monologue: Embodied Reasoning through Planning with Language Models
Wenlong Huang, Fei Xia, Ted Xiao, Harris Chan, Jacky Liang, Pete Florence, Andy Zeng, Jonathan Tompson, Igor Mordatch, Yevgen Chebotar, Pierre Sermanet, Noah Brown, Tomas Jackson, Linda Luu, Sergey Levine, Karol Hausman, Brian Ichter

Additional References

• Large-scale simulation for embodied perception and robot learning, Xia 2021
• QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation, Kalashnikov et al 2018
• MT-Opt: Continuous Multi-Task Robotic Reinforcement Learning at Scale, Kalashnikov et al 2021
• ReLMoGen: Leveraging Motion Generation in Reinforcement Learning for Mobile Manipulation, Xia et al 2020
• Actionable Models: Unsupervised Offline Reinforcement Learning of Robotic Skills, Chebotar et al 2021  
• Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language, Zeng et al 2022

Episode sponsor: Anyscale

Ray Summit 2022 is coming to San Francisco on August 23-24.
Hear how teams at Dow, Verizon, Riot Games, and more are solving their RL challenges with Ray's RLlib.

Register at raysummit.org and use code RAYSUMMIT22RL for a further 25% off the already reduced prices.