TalkRL: The Reinforcement Learning Podcast

Latest Episodes:

Aravind Srinivas 2 May 08, 2022

Show notes

Aravind Srinivas is back! He is now a research Scientist at OpenAI.

Featured References

Decision Transformer: Reinforcement Learning via Sequence Modeling
Lili Chen, Kevin Lu, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Michael Laskin, Pieter Abbeel, Aravind Srinivas, Igor Mordatch

VideoGPT: Video Generation using VQ-VAE and Transformers
Wilson Yan, Yunzhi Zhang, Pieter Abbeel, Aravind Srinivas

Rohin Shah Apr 11, 2022

Show notes

Dr. Rohin Shah is a Research Scientist at DeepMind, and the editor and main contributor of the Alignment Newsletter.

Featured References

The MineRL BASALT Competition on Learning from Human Feedback
Rohin Shah, Cody Wild, Steven H. Wang, Neel Alex, Brandon Houghton, William Guss, Sharada Mohanty, Anssi Kanervisto, Stephanie Milani, Nicholay Topin, Pieter Abbeel, Stuart Russell, Anca Dragan

Preferences Implicit in the State of the World
Rohin Shah, Dmitrii Krasheninnikov, Jordan Alexander, Pieter Abbeel, Anca Dragan

Benefits of Assistance over Reward Learning
Rohin Shah, Pedro Freire, Neel Alex, Rachel Freedman, Dmitrii Krasheninnikov, Lawrence Chan, Michael D Dennis, Pieter Abbeel, Anca Dragan, Stuart Russell

On the Utility of Learning about Humans for Human-AI Coordination
Micah Carroll, Rohin Shah, Mark K. Ho, Thomas L. Griffiths, Sanjit A. Seshia, Pieter Abbeel, Anca Dragan

Evaluating the Robustness of Collaborative Agents
Paul Knott, Micah Carroll, Sam Devlin, Kamil Ciosek, Katja Hofmann, A. D. Dragan, Rohin Shah

Additional References

AGI Safety Fundamentals, EA Cambridge

Robert Lange Dec 20, 2021

Show notes

Robert Tjarko Lange is a PhD student working at the Technical University Berlin.

Featured References

Learning not to learn: Nature versus nurture in silico
Lange, R. T., & Sprekeler, H. (2020)

On Lottery Tickets and Minimal Task Representations in Deep Reinforcement Learning
Vischer, M. A., Lange, R. T., & Sprekeler, H. (2021).

Semantic RL with Action Grammars: Data-Efficient Learning of Hierarchical Task Abstractions
Lange, R. T., & Faisal, A. (2019).

MLE-Infrastructure on Github

Additional References

RL^2: Fast Reinforcement Learning via Slow Reinforcement Learning, Duan et al 2016
Learning to reinforcement learn, Wang et al 2016
Decision Transformer: Reinforcement Learning via Sequence Modeling, Chen et al 2021

NeurIPS 2021 Political Economy of Reinforcement Learning Systems (PERLS) Workshop Nov 18, 2021

Show notes

We hear about the idea of PERLS and why its important to talk about.

Political Economy of Reinforcement Learning (PERLS) Workshop at NeurIPS 2021 on Tues Dec 14th
NeurIPS 2021

Amy Zhang Sep 27, 2021

Show notes

Amy Zhang is a postdoctoral scholar at UC Berkeley and a research scientist at Facebook AI Research. She will be starting as an assistant professor at UT Austin in Spring 2023.

Featured References

Invariant Causal Prediction for Block MDPs
Amy Zhang, Clare Lyle, Shagun Sodhani, Angelos Filos, Marta Kwiatkowska, Joelle Pineau, Yarin Gal, Doina Precup

Multi-Task Reinforcement Learning with Context-based Representations
Shagun Sodhani, Amy Zhang, Joelle Pineau

MBRL-Lib: A Modular Library for Model-based Reinforcement Learning
Luis Pineda, Brandon Amos, Amy Zhang, Nathan O. Lambert, Roberto Calandra

Additional References

Xianyuan Zhan Aug 30, 2021

Show notes

Xianyuan Zhan is currently a research assistant professor at the Institute for AI Industry Research (AIR), Tsinghua University. He received his Ph.D. degree at Purdue University. Before joining Tsinghua University, Dr. Zhan worked as a researcher at Microsoft Research Asia (MSRA) and a data scientist at JD Technology. At JD Technology, he led the research that uses offline RL to optimize real-world industrial systems.

Featured References

DeepThermal: Combustion Optimization for Thermal Power Generating Units Using Offline Reinforcement Learning
Xianyuan Zhan, Haoran Xu, Yue Zhang, Yusen Huo, Xiangyu Zhu, Honglei Yin, Yu Zheng

Eugene Vinitsky Aug 18, 2021

Show notes

Eugene Vinitsky is a PhD student at UC Berkeley advised by Alexandre Bayen. He has interned at Tesla and Deepmind.

Featured References

A learning agent that acquires social norms from public sanctions in decentralized multi-agent settings
Eugene Vinitsky, Raphael Köster, John P. Agapiou, Edgar Duéñez-Guzmán, Alexander Sasha Vezhnevets, Joel Z. Leibo

Optimizing Mixed Autonomy Traffic Flow With Decentralized Autonomous Vehicles and Multi-Agent RL
Eugene Vinitsky, Nathan Lichtle, Kanaad Parvate, Alexandre Bayen

Lagrangian Control through Deep-RL: Applications to Bottleneck Decongestion
Eugene Vinitsky; Kanaad Parvate; Aboudy Kreidieh; Cathy Wu; Alexandre Bayen 2018

The Surprising Effectiveness of PPO in Cooperative Multi-Agent Games
Chao Yu, Akash Velu, Eugene Vinitsky, Yu Wang, Alexandre Bayen, Yi Wu

Additional References

SUMO: Simulation of Urban MObility

Jess Whittlestone Jul 20, 2021

Show notes

Dr. Jess Whittlestone is a Senior Research Fellow at the Centre for the Study of Existential Risk and the Leverhulme Centre for the Future of Intelligence, both at the University of Cambridge.

Featured References

The Societal Implications of Deep Reinforcement Learning
Jess Whittlestone, Kai Arulkumaran, Matthew Crosby

Artificial Canaries: Early Warning Signs for Anticipatory and Democratic Governance of AI
Carla Zoe Cremer, Jess Whittlestone

Additional References

CogX: Cutting Edge: Understanding AI systems for a better AI policy, featuring Jack Clark and Jess Whittlestone

Aleksandra Faust Jul 06, 2021

Show notes

Dr Aleksandra Faust is a Staff Research Scientist and Reinforcement Learning research team co-founder at Google Brain Research.

Featured References

Reinforcement Learning and Planning for Preference Balancing Tasks
Faust 2014

Learning Navigation Behaviors End-to-End with AutoRL
Hao-Tien Lewis Chiang, Aleksandra Faust, Marek Fiser, Anthony Francis

Evolving Rewards to Automate Reinforcement Learning
Aleksandra Faust, Anthony Francis, Dar Mehta

Evolving Reinforcement Learning Algorithms

John D Co-Reyes, Yingjie Miao, Daiyi Peng, Esteban Real, Quoc V Le, Sergey Levine, Honglak Lee, Aleksandra Faust

Adversarial Environment Generation for Learning to Navigate the Web
Izzeddin Gur, Natasha Jaques, Kevin Malta, Manoj Tiwari, Honglak Lee, Aleksandra Faust

Additional References

AutoML-Zero: Evolving Machine Learning Algorithms From Scratch, Esteban Real, Chen Liang, David R. So, Quoc V. Le

Sam Ritter Jun 21, 2021

Show notes

Sam Ritter is a Research Scientist on the neuroscience team at DeepMind.

Featured References

Unsupervised Predictive Memory in a Goal-Directed Agent (MERLIN)
Greg Wayne, Chia-Chun Hung, David Amos, Mehdi Mirza, Arun Ahuja, Agnieszka Grabska-Barwinska, Jack Rae, Piotr Mirowski, Joel Z. Leibo, Adam Santoro, Mevlana Gemici, Malcolm Reynolds, Tim Harley, Josh Abramson, Shakir Mohamed, Danilo Rezende, David Saxton, Adam Cain, Chloe Hillier, David Silver, Koray Kavukcuoglu, Matt Botvinick, Demis Hassabis, Timothy Lillicrap

Meta-RL without forgetting: Been There, Done That: Meta-Learning with Episodic Recall
Samuel Ritter, Jane X. Wang, Zeb Kurth-Nelson, Siddhant M. Jayakumar, Charles Blundell, Razvan Pascanu, Matthew Botvinick

Meta-Reinforcement Learning with Episodic Recall: An Integrative Theory of Reward-Driven Learning
Samuel Ritter 2019

Meta-RL exploration and planning: Rapid Task-Solving in Novel Environments
Sam Ritter, Ryan Faulkner, Laurent Sartran, Adam Santoro, Matt Botvinick, David Raposo

Synthetic Returns for Long-Term Credit Assignment
David Raposo, Sam Ritter, Adam Santoro, Greg Wayne, Theophane Weber, Matt Botvinick, Hado van Hasselt, Francis Song

Additional References