TalkRL: The Reinforcement Learning Podcast

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 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 

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!