TalkRL: The Reinforcement Learning Podcast

Latest Episodes:

Antonin Raffin and Ashley Hill Sep 04, 2019

Show notes

Antonin Raffin is a researcher at the German Aerospace Center (DLR) in Munich, working in the Institute of Robotics and Mechatronics. His research is on using machine learning for controlling real robots (because simulation is not enough), with a particular interest for reinforcement learning.

Ashley Hill is doing his thesis on improving control algorithms using machine learning for real time gain tuning.

He works mainly with neuroevolution, genetic algorithms, and of course reinforcement learning, applied to mobile robots. He holds a masters degree in Machine learning, and a bachelors in Computer science from the Université Paris-Saclay.

Featured References

stable-baselines on github
Ashley Hill, Antonin Raffin primary authors.

S-RL Toolbox
Antonin Raffin, Ashley Hill, René Traoré, Timothée Lesort, Natalia Díaz-Rodríguez, David Filliat

Decoupling feature extraction from policy learning: assessing benefits of state representation learning in goal based robotics
Antonin Raffin, Ashley Hill, René Traoré, Timothée Lesort, Natalia Díaz-Rodríguez, David Filliat

Additional References

Learning to Drive Smoothly in Minutes, Antonin Raffin
Multimodal SRL (best paper at ICRA): Making Sense of Vision and Touch: Self-Supervised Learning of Multimodal Representations for Contact-Rich Tasks, Michelle A. Lee, Yuke Zhu, Krishnan Srinivasan, Parth Shah, Silvio Savarese, Li Fei-Fei, Animesh Garg, Jeannette Bohg
Benchmarking Model-Based Reinforcement Learning, Tingwu Wang, Xuchan Bao, Ignasi Clavera, Jerrick Hoang, Yeming Wen, Eric Langlois, Shunshi Zhang, Guodong Zhang, Pieter Abbeel, Jimmy Ba
TossingBot: Learning to Throw Arbitrary Objects with Residual Physics
Andy Zeng, Shuran Song, Johnny Lee, Alberto Rodriguez, Thomas Funkhouser
Stable Baselines roadmap
OpenAI baselines stable-baselines github pull request

Michael Littman Aug 23, 2019

Show notes

Michael L Littman is a professor of Computer Science at Brown University. He was elected ACM Fellow in 2018 "For contributions to the design and analysis of sequential decision making algorithms in artificial intelligence".

Featured References

Convergent Actor Critic by Humans
James MacGlashan, Michael L. Littman, David L. Roberts, Robert Tyler Loftin, Bei Peng, Matthew E. Taylor

People teach with rewards and punishments as communication, not reinforcements
Mark Ho, Fiery Cushman, Michael L. Littman, Joseph Austerweil

Theory of Minds: Understanding Behavior in Groups Through Inverse Planning
Michael Shum, Max Kleiman-Weiner, Michael L. Littman, Joshua B. Tenenbaum

Personalized education at scale
Saarinen, Cater, Littman

Additional References

Michael Littman papers on Google Scholar, Semantic Scholar
Reinforcement Learning on Udacity, Charles Isbell, Michael Littman, Chris Pryby
Machine Learning on Udacity, Michael Littman, Charles Isbell, Pushkar Kolhe
Temporal Difference Learning and TD-Gammon, Gerald Tesauro
Playing Atari with Deep Reinforcement Learning, Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, Martin Riedmiller
Ask Me Anything about MOOCs, D Fisher, C Isbell, ML Littman, M Wollowski, et al
Reinforcement Learning and Decision Making (RLDM) Conference
Algorithms for Sequential Decision Making, Michael Littman's Thesis
Machine Learning A Cappella - Overfitting Thriller!, Michael Littman and Charles Isbell feat Infinite Harmony
Turbotax Ad 2016: Genius Anna/Michael Littman

Natasha Jaques Aug 09, 2019

Show notes

Natasha Jaques is a PhD candidate at MIT working on affective and social intelligence. She has interned with DeepMind and Google Brain, and was an OpenAI Scholars mentor. Her paper “Social Influence as Intrinsic Motivation for Multi-Agent Deep Reinforcement Learning” received an honourable mention for best paper at ICML 2019.

Featured References

Social Influence as Intrinsic Motivation for Multi-Agent Deep Reinforcement Learning
Natasha Jaques, Angeliki Lazaridou, Edward Hughes, Caglar Gulcehre, Pedro A. Ortega, DJ Strouse, Joel Z. Leibo, Nando de Freitas

Tackling climate change with Machine Learning
David Rolnick, Priya L. Donti, Lynn H. Kaack, Kelly Kochanski, Alexandre Lacoste, Kris Sankaran, Andrew Slavin Ross, Nikola Milojevic-Dupont, Natasha Jaques, Anna Waldman-Brown, Alexandra Luccioni, Tegan Maharaj, Evan D. Sherwin, S. Karthik Mukkavilli, Konrad P. Kording, Carla Gomes, Andrew Y. Ng, Demis Hassabis, John C. Platt, Felix Creutzig, Jennifer Chayes, Yoshua Bengio

Additional References

MIT Media Lab Flight Offsets, Caroline Jaffe, Juliana Cherston, Natasha Jaques
Modeling Others using Oneself in Multi-Agent Reinforcement Learning,
Roberta Raileanu, Emily Denton, Arthur Szlam, Rob Fergus
Inequity aversion improves cooperation in intertemporal social dilemmas,
Edward Hughes, Joel Z. Leibo, Matthew G. Phillips, Karl Tuyls, Edgar A. Duéñez-Guzmán, Antonio García Castañeda, Iain Dunning, Tina Zhu, Kevin R. McKee, Raphael Koster, Heather Roff, Thore Graepel
Sequential Social Dilemma Games on github, Eugene Vinitsky, Natasha Jaques
AI Alignment newsletter, Rohin Shah
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
The social function of intellect, Nicholas Humphrey
Autocurricula and the Emergence of Innovation from Social Interaction: A Manifesto for Multi-Agent Intelligence Research, Joel Z. Leibo, Edward Hughes, Marc Lanctot, Thore Graepel
A Recipe for Training Neural Networks, Andrej Karpathy
Emotionally Adaptive Intelligent Tutoring Systems using POMDPs, Natasha Jaques
Sapiens, Yuval Noah Harari

About TalkRL Podcast: All Reinforcement Learning, All the Time Aug 01, 2019

Show notes

August 2, 2019

Transcript

The idea with TalkRL Podcast is to hear from brilliant folks from across the world of Reinforcement Learning, both research and applications. As much as possible, I want to hear from them in their own language. I try to get to know as much as I can about their work before hand.

And Im not here to convert anyone, I want to reach people who are already into RL. So we wont stop to explain what a value function is, for example. Though we also wont assume everyone has read the very latest papers.

Why am I doing this? Because it’s a great way to learn from the most inspiring people in the field! There’s so much happening in the universe of RL, and there’s tons of interesting angles and so many fascinating minds to learn from.

Now I know there is no shortage of books, papers, and lectures, but so much goes unsaid.

I mean I guess if you work at MILA or AMII or Vector Institute, you might be having these conversations over coffee all the time, but I live in a little village in the woods in BC, so for me, these remote interviews are like a great way to have these conversations, and I hope sharing with the community makes it more worthwhile for everyone.

In terms of format, the first 2 episodes were interviews in longer form, around an hour long. Going forward, some may be a lot shorter, it depends on the guest.

If you want want to be a guest or suggest a guest, goto talkrl.com/about, you will find a link to a suggestion form.

Thanks for listening!