TalkRL: The Reinforcement Learning Podcast

Professor Marc G. Bellemare is a Research Scientist at Google Research (Brain team), An Adjunct Professor at McGill University, and a Canada CIFAR AI Chair.  Featured References  The Arcade Learning Environment: An Evaluation Platform for General Agents   Marc G. Bellemare, Yavar Naddaf, Joel Veness, Michael Bowling  Human-level control through deep reinforcement learning   Volodymyr Mnih, Koray K...

Robert Osazuwa Ness is an adjunct professor of computer science at Northeastern University, an ML Research Engineer at Gamalon , and the founder of AltDeep School of AI .  He holds a PhD in statistics.  He studied at Johns Hopkins SAIS and then Purdue University.  References  Altdeep School of AI , Altdeep on Twitch , Substack , Robert Ness  Altdeep Causal Generative Machine Learning Minicourse ,...

Dr. Marlos C. Machado is a research scientist at DeepMind and an adjunct professor at the University of Alberta. He holds a PhD from the University of Alberta and a MSc and BSc from UFMG, in Brazil.  Featured References  Revisiting the Arcade Learning Environment: Evaluation Protocols and Open Problems for General Agents  Marlos C. Machado, Marc G. Bellemare, Erik Talvitie, Joel Veness, Matthew J....

Nathan Lambert is a PhD Candidate at UC Berkeley.  Featured References  Learning Accurate Long-term Dynamics for Model-based Reinforcement Learning  Nathan O. Lambert, Albert Wilcox, Howard Zhang, Kristofer S. J. Pister, Roberto Calandra  Objective Mismatch in Model-based Reinforcement Learning  Nathan Lambert, Brandon Amos, Omry Yadan, Roberto Calandra  Low Level Control of a Quadrotor with Deep...

Kai Arulkumaran is a researcher at Araya in Tokyo.  Featured References  AlphaStar: An Evolutionary Computation Perspective   Kai Arulkumaran, Antoine Cully, Julian Togelius  Analysing Deep Reinforcement Learning Agents Trained with Domain Randomisation   Tianhong Dai, Kai Arulkumaran, Tamara Gerbert, Samyakh Tukra, Feryal Behbahani, Anil Anthony Bharath  Training Agents using Upside-Down Reinforc...

Michael Dennis is a PhD student at the Center for Human-Compatible AI at UC Berkeley, supervised by Professor Stuart Russell .  I'm interested in robustness in RL and multi-agent RL, specifically as it applies to making the interaction between AI systems and society at large to be more beneficial.    --Michael Dennis  Featured References Emergent Complexity and Zero-shot Transfer via Unsupervised...

Roman Ring is a Research Engineer at DeepMind.  Featured References  Grandmaster level in StarCraft II using multi-agent reinforcement learning   Vinyals et al, 2019  Replicating DeepMind StarCraft II Reinforcement Learning Benchmark with Actor-Critic Methods   Roman Ring, 2018  Additional References  Relational Deep Reinforcement Learning ,  Zambaldi et al 2018  StarCraft II: A New Challenge for...

Shimon Whiteson is a Professor of Computer Science at Oxford University, the head of WhiRL, the Whiteson Research Lab at Oxford, and Head of Research at Waymo UK.  Featured References  VariBAD: A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning  Luisa Zintgraf, Kyriacos Shiarlis, Maximilian Igl, Sebastian Schulze, Yarin Gal, Katja Hofmann, Shimon Whiteson  Monotonic Value Function Fac...

Aravind Srinivas is a 3rd year PhD student at UC Berkeley advised by Prof. Abbeel.  He co-created and co-taught a grad course on Deep Unsupervised Learning at Berkeley.  Featured References  Data-Efficient Image Recognition with Contrastive Predictive Coding  Olivier J. Hénaff, Aravind Srinivas, Jeffrey De Fauw, Ali Razavi, Carl Doersch, S. M. Ali Eslami, Aaron van den Oord  Contrastive Unsupervis...

Taylor Killian is a Ph. D. student at the University of Toronto and the Vector Institute, and an Intern at Google Brain. Featured References  Direct Policy Transfer with Hidden Parameter Markov Decision Processes Yao, Killian, Konidaris, Doshi-Velez  Robust and Efficient Transfer Learning with Hidden Parameter Markov Decision Processes Killian, Daulton, Konidaris, Doshi-Velez  Transfer Learning Ac...

Nan Jiang is an Assistant Professor of Computer Science at University of Illinois.  He was a Postdoc Microsoft Research, and did his PhD at University of Michigan under Professor Satinder Singh.  Featured References  Reinforcement Learning: Theory and Algorithms Alekh Agarwal Nan Jiang Sham M. Kakade  Model-based RL in Contextual Decision Processes: PAC bounds and Exponential Improvements over Mod...

Danijar Hafner is a PhD student at the University of Toronto, and a student researcher at Google Research, Brain Team and the Vector Institute.  He holds a Masters of Research from University College London.  Featured References  A deep learning framework for neuroscience Blake A. Richards, Timothy P. Lillicrap , Philippe Beaudoin, Yoshua Bengio, Rafal Bogacz, Amelia Christensen, Claudia Clopath,...

Csaba Szepesvari is:  Head of the Foundations Team at DeepMind  Professor of Computer Science at the University of Alberta  Canada CIFAR AI Chair  Fellow at the Alberta Machine Intelligence Institute   Co-Author of the book Bandit Algorithms along with Tor Lattimore, and author of the book Algorithms for Reinforcement Learning   References  Bandit based monte-carlo planning , Levente Kocsis, Csaba...

Ben Eysenbach is a PhD student in the Machine Learning Department at Carnegie Mellon University.  He was a Resident at Google Brain, and studied math and computer science at MIT. He co-founded the ICML Exploration in Reinforcement Learning workshop .  Featured References Diversity is All You Need: Learning Skills without a Reward Function Benjamin Eysenbach, Abhishek Gupta, Julian Ibarz, Sergey Le...

Thank you to all the presenters that participated.  I covered as many as I could given the time and crowds, if you were not included and wish to be, please email talkrl@pathwayi.com  More details on the official NeurIPS Deep RL Workshop site .  0:23  Approximating two value functions instead of one: towards characterizing a new family of Deep Reinforcement Learning algorithms ; Matthia Sabatelli (...

Scott Fujimoto is a PhD student at McGill University and Mila. He is the author of TD3 as well as some of the recent developments in batch deep reinforcement learning.   Featured References  Addressing Function Approximation Error in Actor-Critic Methods   Scott Fujimoto, Herke van Hoof, David Meger  Off-Policy Deep Reinforcement Learning without Exploration   Scott Fujimoto, David Meger, Doina Pr...

Dr. Jessica Hamrick is a Research Scientist at DeepMind. She holds a PhD in Psychology from UC Berkeley.  Featured References  Structured agents for physical construction   Victor Bapst, Alvaro Sanchez-Gonzalez, Carl Doersch, Kimberly L. Stachenfeld, Pushmeet Kohli, Peter W. Battaglia, Jessica B. Hamrick  Analogues of mental simulation and imagination in deep learning   Jessica Hamrick  Additional...

Dr Pablo Samuel Castro is a Staff Research Software Engineer at Google Brain.  He is the main author of the Dopamine RL framework .  Featured References  A Comparative Analysis of Expected and Distributional Reinforcement Learning   Clare Lyle, Pablo Samuel Castro, Marc G. Bellemare   A Geometric Perspective on Optimal Representations for Reinforcement Learning   Marc G. Bellemare, Will Dabney, Ro...

Dr. Kamyar Azizzadenesheli is a post-doctorate scholar at Caltech.  His research interest is mainly in the area of Machine Learning, from theory to practice, with the main focus in Reinforcement Learning.  He will be joining Purdue University as an Assistant CS Professor in Fall 2020.  Featured References  Efficient Exploration through Bayesian Deep Q-Networks  Kamyar Azizzadenesheli, Animashree A...

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 gai...

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 teac...

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 M...

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...