TalkRL: The Reinforcement Learning Podcast

Jordan Terry is a PhD candidate at University of Maryland, the maintainer of Gym, the maintainer and creator of PettingZoo and the founder of Swarm Labs.Featured ReferencesPettingZoo: Gym for Multi-Agent Reinforcement LearningJ. K. Terry, Benjamin Black, Nathaniel Grammel, Mario Jayakumar, Ananth Hari, Ryan Sullivan, Luis Santos, Rodrigo Perez, Caroline Horsch, Clemens Dieffendahl, Niall L. Williams, Yashas Lokesh, Praveen RaviPettingZoo on Githubgym on GithubAdditional ReferencesTime Limits in Reinforcement Learning, Pardo et al 2017Deep Reinforcement Learning at the Edge of the Statistical Precipice, Agarwal et al 2021

Robert Tjarko Lange, a PhD student at TU Berlin, discusses topics like meta reinforcement learning, hard-coded behaviors in animals, lottery ticket hypothesis and pruning masks in deep RL, semantic RL with action grammars, advances in meta RL, the need for scientific governance, and exploring the role of parameterization in RL.

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 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 Amy Zhang - Exploring Context for Better Generalization in Reinforcement Learning @ UCL DARK ICML 2020 Poster session: Invariant Causal Prediction for Block MDPs Clare Lyle - Invariant Prediction for Generalization in Reinforcement Learning @ Simons Institute 

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 LearningXianyuan Zhan, Haoran Xu, Yue Zhang, Yusen Huo, Xiangyu Zhu, Honglei Yin, Yu Zheng 

Eugene Vinitsky, a PhD student at UC Berkeley with experience at Tesla and DeepMind, explores groundbreaking applications of reinforcement learning in transportation. He discusses enhancing cruise control systems through cooperative AI behaviors, tackling traffic management challenges, and optimizing flow using decentralized systems. Vinitsky also dives into traffic simulations with Sumo, the effectiveness of PPO in multi-agent settings, and how AI can navigate social dilemmas like climate change. His insights illuminate the future of smart, efficient transportation.

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 

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 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 Sam Ritter: Meta-Learning to Make Smart Inferences from Small Data , North Star AI 2019 The Bitter Lesson, Rich Sutton 2019 

Thomas Krendl Gilbert is a PhD student at UC Berkeley’s Center for Human-Compatible AI, specializing in Machine Ethics and Epistemology. Featured References Hard Choices in Artificial Intelligence: Addressing Normative Uncertainty through Sociotechnical Commitments Roel Dobbe, Thomas Krendl Gilbert, Yonatan Mintz Mapping the Political Economy of Reinforcement Learning Systems: The Case of Autonomous Vehicles Thomas Krendl Gilbert AI Development for the Public Interest: From Abstraction Traps to Sociotechnical Risks McKane Andrus, Sarah Dean, Thomas Krendl Gilbert, Nathan Lambert and Tom Zick Additional References Political Economy of Reinforcement Learning Systems (PERLS) The Law and Political Economy (LPE) Project The Societal Implications of Deep Reinforcement Learning, Jess Whittlestone, Kai Arulkumaran, Matthew Crosby Robot Brains Podcast: Yann LeCun explains why Facebook would crumble without AI