TalkRL: The Reinforcement Learning Podcast

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 LearningNatasha Jaques, Angeliki Lazaridou, Edward Hughes, Caglar Gulcehre, Pedro A. Ortega, DJ Strouse, Joel Z. Leibo, Nando de Freitas

Tackling climate change with Machine LearningDavid 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

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!

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.

Featured References

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 researcherPierluca D'Oro

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 :)

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.