TalkRL: The Reinforcement Learning Podcast

Saikrishna Gottipati is an RL Researcher at AI Redefined, working on RL, MARL, human in the loop learning.Featured ReferencesCogment: Open Source Framework For Distributed Multi-actor Training, Deployment & OperationsAI Redefined, Sai Krishna Gottipati, Sagar Kurandwad, Clodéric Mars, Gregory Szriftgiser, François ChabotDo As You Teach: A Multi-Teacher Approach to Self-Play in Deep Reinforcement LearningCurrently under reviewLearning to navigate the synthetically accessible chemical space using reinforcement learningSai Krishna Gottipati, Boris Sattarov, Sufeng Niu, Yashaswi Pathak, Haoran Wei, Shengchao Liu, Karam J. Thomas, Simon Blackburn, Connor W. Coley, Jian Tang, Sarath Chandar, Yoshua BengioAdditional ReferencesAsymmetric self-play for automatic goal discovery in robotic manipulation, 2021 OpenAI et al Continuous Coordination As a Realistic Scenario for Lifelong Learning, 2021 Nekoei et alEpisode sponsor: AnyscaleRay 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.

Aravind Srinivas is back!  He is now a research Scientist at OpenAI.Featured ReferencesDecision Transformer: Reinforcement Learning via Sequence ModelingLili Chen, Kevin Lu, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Michael Laskin, Pieter Abbeel, Aravind Srinivas, Igor MordatchVideoGPT: Video Generation using VQ-VAE and TransformersWilson Yan, Yunzhi Zhang, Pieter Abbeel, Aravind Srinivas

Dr. Rohin Shah is a Research Scientist at DeepMind, and the editor and main contributor of the Alignment Newsletter.Featured ReferencesThe MineRL BASALT Competition on Learning from Human FeedbackRohin 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 DraganPreferences Implicit in the State of the WorldRohin Shah, Dmitrii Krasheninnikov, Jordan Alexander, Pieter Abbeel, Anca DraganBenefits 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 RussellOn the Utility of Learning about Humans for Human-AI CoordinationMicah Carroll, Rohin Shah, Mark K. Ho, Thomas L. Griffiths, Sanjit A. Seshia, Pieter Abbeel, Anca DraganEvaluating the Robustness of Collaborative AgentsPaul Knott, Micah Carroll, Sam Devlin, Kamil Ciosek, Katja Hofmann, A. D. Dragan, Rohin ShahAdditional ReferencesAGI Safety Fundamentals, EA Cambridge

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