Podcast TalkRL: The Reinforcement Learning Podcast by Robin Ranjit Singh Chauhan Episodes

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	Title	Pub. Date	Duration
	Neurips 2024 RL meetup Hot takes: What sucks about RL?	23 Dec 2024	00:17:45
What do RL researchers complain about after hours at the bar? In this "Hot takes" episode, we find out! Recorded at The Pearl in downtown Vancouver, during the RL meetup after a day of Neurips 2024. Special thanks to "David Beckham" for the inspiration :)
	RLC 2024 - Posters and Hallways 5	20 Sep 2024	00:13:17
Posters and Hallway episodes are short interviews and poster summaries. Recorded at RLC 2024 in Amherst MA. Featuring: 0:01 David Radke of the Chicago Blackhawks NHL on RL for professional sports 0:56 Abhishek Naik from the National Research Council on Continuing RL and Average Reward 2:42 Daphne Cornelisse from NYU on Autonomous Driving and Multi-Agent RL 08:58 Shray Bansal from Georgia Tech on Cognitive Bias for Human AI Ad hoc Teamwork 10:21 Claas Voelcker from University of Toronto on Can we hop in general? 11:23 Brent Venable from The Institute for Human & Machine Cognition on Cooperative information dissemination
	Arash Ahmadian on Rethinking RLHF	25 Mar 2024	00:33:30
Arash Ahmadian is a Researcher at Cohere and Cohere For AI focussed on Preference Training of large language models. He’s also a researcher at the Vector Institute of AI. Featured Reference Back to Basics: Revisiting REINFORCE Style Optimization for Learning from Human Feedback in LLMs Arash Ahmadian, Chris Cremer, Matthias Gallé, Marzieh Fadaee, Julia Kreutzer, Olivier Pietquin, Ahmet Üstün, Sara Hooker Additional References Self-Rewarding Language Models, Yuan et al 2024 Reinforcement Learning: An Introduction, Sutton and Barto 1992 Learning from Delayed Rewards, Chris Watkins 1989 Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning, Williams 1992
	Glen Berseth on RL Conference	11 Mar 2024	00:21:38
Glen Berseth is an assistant professor at the Université de Montréal, a core academic member of the Mila - Quebec AI Institute, a Canada CIFAR AI chair, member l'Institute Courtios, and co-director of the Robotics and Embodied AI Lab (REAL). Featured Links Reinforcement Learning Conference Closing the Gap between TD Learning and Supervised Learning--A Generalisation Point of View Raj Ghugare, Matthieu Geist, Glen Berseth, Benjamin Eysenbach
	Ian Osband	07 Mar 2024	01:08:26
Ian Osband is a Research scientist at OpenAI (ex DeepMind, Stanford) working on decision making under uncertainty. We spoke about: - Information theory and RL - Exploration, epistemic uncertainty and joint predictions - Epistemic Neural Networks and scaling to LLMs Featured References Reinforcement Learning, Bit by Bit Xiuyuan Lu, Benjamin Van Roy, Vikranth Dwaracherla, Morteza Ibrahimi, Ian Osband, Zheng Wen From Predictions to Decisions: The Importance of Joint Predictive Distributions Zheng Wen, Ian Osband, Chao Qin, Xiuyuan Lu, Morteza Ibrahimi, Vikranth Dwaracherla, Mohammad Asghari, Benjamin Van Roy Epistemic Neural Networks Ian Osband, Zheng Wen, Seyed Mohammad Asghari, Vikranth Dwaracherla, Morteza Ibrahimi, Xiuyuan Lu, Benjamin Van Roy Approximate Thompson Sampling via Epistemic Neural Networks Ian Osband, Zheng Wen, Seyed Mohammad Asghari, Vikranth Dwaracherla, Morteza Ibrahimi, Xiuyuan Lu, Benjamin Van Roy Additional References Thesis defence, Ian Osband Homepage, Ian Osband Epistemic Neural Networks at Stanford RL Forum Behaviour Suite for Reinforcement Learning, Osband et al 2019 Efficient Exploration for LLMs, Dwaracherla et al 2024
	Sharath Chandra Raparthy	12 Feb 2024	00:40:41
Sharath Chandra Raparthy on In-Context Learning for Sequential Decision Tasks, GFlowNets, and more! Sharath Chandra Raparthy is an AI Resident at FAIR at Meta, and did his Master's at Mila. Featured Reference Generalization to New Sequential Decision Making Tasks with In-Context Learning Sharath Chandra Raparthy , Eric Hambro, Robert Kirk , Mikael Henaff, , Roberta Raileanu Additional References Sharath Chandra Raparthy Homepage Human-Timescale Adaptation in an Open-Ended Task Space, Adaptive Agent Team 2023 Data Distributional Properties Drive Emergent In-Context Learning in Transformers, Chan et al 2022 Decision Transformer: Reinforcement Learning via Sequence Modeling, Chen et al 2021
	Pierluca D'Oro and Martin Klissarov	13 Nov 2023	00:57:24
Pierluca D'Oro and Martin Klissarov on Motif and RLAIF, Noisy Neighborhoods and Return Landscapes, and more! Pierluca D'Oro is PhD student at Mila and visiting researcher at Meta. Martin Klissarov is a PhD student at Mila and McGill and research scientist intern at Meta. Featured References Motif: Intrinsic Motivation from Artificial Intelligence Feedback Martin Klissarov, Pierluca D'Oro, Shagun Sodhani, Roberta Raileanu, Pierre-Luc Bacon, Pascal Vincent, Amy Zhang, Mikael Henaff Policy Optimization in a Noisy Neighborhood: On Return Landscapes in Continuous Control Nate Rahn, Pierluca D'Oro, Harley Wiltzer, Pierre-Luc Bacon, Marc G. Bellemare To keep doing RL research, stop calling yourself an RL researcher Pierluca D'Oro
	Martin Riedmiller	22 Aug 2023	01:13:56
Martin Riedmiller of Google DeepMind on controlling nuclear fusion plasma in a tokamak with RL, the original Deep Q-Network, Neural Fitted Q-Iteration, Collect and Infer, AGI for control systems, and tons more! Martin Riedmiller is a research scientist and team lead at DeepMind. Featured References Magnetic control of tokamak plasmas through deep reinforcement learning Jonas Degrave, Federico Felici, Jonas Buchli, Michael Neunert, Brendan Tracey, Francesco Carpanese, Timo Ewalds, Roland Hafner, Abbas Abdolmaleki, Diego de las Casas, Craig Donner, Leslie Fritz, Cristian Galperti, Andrea Huber, James Keeling, Maria Tsimpoukelli, Jackie Kay, Antoine Merle, Jean-Marc Moret, Seb Noury, Federico Pesamosca, David Pfau, Olivier Sauter, Cristian Sommariva, Stefano Coda, Basil Duval, Ambrogio Fasoli, Pushmeet Kohli, Koray Kavukcuoglu, Demis Hassabis & Martin Riedmiller Human-level control through deep reinforcement learning Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski, Stig Petersen, Charles Beattie, Amir Sadik, Ioannis Antonoglou, Helen King, Dharshan Kumaran, Daan Wierstra, Shane Legg, Demis Hassabis Neural fitted Q iteration–first experiences with a data efficient neural reinforcement learning method Martin Riedmiller
	Max Schwarzer	08 Aug 2023	01:10:18
Max Schwarzer is a PhD student at Mila, with Aaron Courville and Marc Bellemare, interested in RL scaling, representation learning for RL, and RL for science. Max spent the last 1.5 years at Google Brain/DeepMind, and is now at Apple Machine Learning Research. Featured References Bigger, Better, Faster: Human-level Atari with human-level efficiency Max Schwarzer, Johan Obando-Ceron, Aaron Courville, Marc Bellemare, Rishabh Agarwal, Pablo Samuel Castro Sample-Efficient Reinforcement Learning by Breaking the Replay Ratio Barrier Pierluca D'Oro, Max Schwarzer, Evgenii Nikishin, Pierre-Luc Bacon, Marc G Bellemare, Aaron Courville The Primacy Bias in Deep Reinforcement Learning Evgenii Nikishin, Max Schwarzer, Pierluca D'Oro, Pierre-Luc Bacon, Aaron Courville Additional References Rainbow: Combining Improvements in Deep Reinforcement Learning, Hessel et al 2017 When to use parametric models in reinforcement learning? Hasselt et al 2019 Data-Efficient Reinforcement Learning with Self-Predictive Representations, Schwarzer et al 2020 Pretraining Representations for Data-Efficient Reinforcement Learning, Schwarzer et al 2021
	Julian Togelius	25 Jul 2023	00:40:04
Julian Togelius is an Associate Professor of Computer Science and Engineering at NYU, and Cofounder and research director at modl.ai Featured References Choose Your Weapon: Survival Strategies for Depressed AI Academics Julian Togelius, Georgios N. Yannakakis Learning Controllable 3D Level Generators Zehua Jiang, Sam Earle, Michael Cerny Green, Julian Togelius PCGRL: Procedural Content Generation via Reinforcement Learning Ahmed Khalifa, Philip Bontrager, Sam Earle, Julian Togelius Illuminating Generalization in Deep Reinforcement Learning through Procedural Level Generation Niels Justesen, Ruben Rodriguez Torrado, Philip Bontrager, Ahmed Khalifa, Julian Togelius, Sebastian Risi
	Jakob Foerster	08 May 2023	01:03:45
Jakob Foerster on Multi-Agent learning, Cooperation vs Competition, Emergent Communication, Zero-shot coordination, Opponent Shaping, agents for Hanabi and Prisoner's Dilemma, and more. Jakob Foerster is an Associate Professor at University of Oxford. Featured References Learning with Opponent-Learning Awareness Jakob N. Foerster, Richard Y. Chen, Maruan Al-Shedivat, Shimon Whiteson, Pieter Abbeel, Igor Mordatch Model-Free Opponent Shaping Chris Lu, Timon Willi, Christian Schroeder de Witt, Jakob Foerster Off-Belief Learning Hengyuan Hu, Adam Lerer, Brandon Cui, David Wu, Luis Pineda, Noam Brown, Jakob Foerster Learning to Communicate with Deep Multi-Agent Reinforcement Learning Jakob N. Foerster, Yannis M. Assael, Nando de Freitas, Shimon Whiteson Adversarial Cheap Talk Chris Lu, Timon Willi, Alistair Letcher, Jakob Foerster Cheap Talk Discovery and Utilization in Multi-Agent Reinforcement Learning Yat Long Lo, Christian Schroeder de Witt, Samuel Sokota, Jakob Nicolaus Foerster, Shimon Whiteson Additional References Lectures by Jakob on youtube
	Danijar Hafner 2	12 Apr 2023	00:45:21
Danijar Hafner on the DreamerV3 agent and world models, the Director agent and heirarchical RL, realtime RL on robots with DayDreamer, and his framework for unsupervised agent design! Danijar Hafner is a PhD candidate at the University of Toronto with Jimmy Ba, a visiting student at UC Berkeley with Pieter Abbeel, and an intern at DeepMind. He has been our guest before back on episode 11. Featured References Mastering Diverse Domains through World Models [ blog ] DreaverV3 Danijar Hafner, Jurgis Pasukonis, Jimmy Ba, Timothy Lillicrap DayDreamer: World Models for Physical Robot Learning [ blog ] Philipp Wu, Alejandro Escontrela, Danijar Hafner, Ken Goldberg, Pieter Abbeel Deep Hierarchical Planning from Pixels [ blog ] Danijar Hafner, Kuang-Huei Lee, Ian Fischer, Pieter Abbeel Action and Perception as Divergence Minimization [ blog ] Danijar Hafner, Pedro A. Ortega, Jimmy Ba, Thomas Parr, Karl Friston, Nicolas Heess Additional References Mastering Atari with Discrete World Models [ blog ] DreaverV2 ; Danijar Hafner, Timothy Lillicrap, Mohammad Norouzi, Jimmy Ba Dream to Control: Learning Behaviors by Latent Imagination [ blog ] Dreamer ; Danijar Hafner, Timothy Lillicrap, Jimmy Ba, Mohammad Norouzi Planning to Explore via Self-Supervised World Models ; Ramanan Sekar, Oleh Rybkin, Kostas Daniilidis, Pieter Abbeel, Danijar Hafner, Deepak Pathak
	RLC 2024 - Posters and Hallways 4	19 Sep 2024	00:04:52
Posters and Hallway episodes are short interviews and poster summaries. Recorded at RLC 2024 in Amherst MA. Featuring: 0:01 David Abel from DeepMind on 3 Dogmas of RL 0:55 Kevin Wang from Brown on learning variable depth search for MCTS 2:17 Ashwin Kumar from Washington University in St Louis on fairness in resource allocation 3:36 Prabhat Nagarajan from UAlberta on Value overestimation
	Jeff Clune	27 Mar 2023	01:11:11
AI Generating Algos, Learning to play Minecraft with Video PreTraining (VPT), Go-Explore for hard exploration, POET and Open Endedness, AI-GAs and ChatGPT, AGI predictions, and lots more! Professor Jeff Clune is Associate Professor of Computer Science at University of British Columbia, a Canada CIFAR AI Chair and Faculty Member at Vector Institute, and Senior Research Advisor at DeepMind. Featured References Video PreTraining (VPT): Learning to Act by Watching Unlabeled Online Videos [ Blog Post ] Bowen Baker, Ilge Akkaya, Peter Zhokhov, Joost Huizinga, Jie Tang, Adrien Ecoffet, Brandon Houghton, Raul Sampedro, Jeff Clune Robots that can adapt like animals Antoine Cully, Jeff Clune, Danesh Tarapore, Jean-Baptiste Mouret Illuminating search spaces by mapping elites Jean-Baptiste Mouret, Jeff Clune Enhanced POET: Open-Ended Reinforcement Learning through Unbounded Invention of Learning Challenges and their Solutions Rui Wang, Joel Lehman, Aditya Rawal, Jiale Zhi, Yulun Li, Jeff Clune, Kenneth O. Stanley 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 First return, then explore Adrien Ecoffet, Joost Huizinga, Joel Lehman, Kenneth O. Stanley, Jeff Clune
	Natasha Jaques 2	14 Mar 2023	00:46:02
Hear about why OpenAI cites her work in RLHF and dialog models, approaches to rewards in RLHF, ChatGPT, Industry vs Academia, PsiPhi-Learning, AGI and more! Dr Natasha Jaques is a Senior Research Scientist at Google Brain. Featured References Way Off-Policy Batch Deep Reinforcement Learning of Implicit Human Preferences in Dialog Natasha Jaques, Asma Ghandeharioun, Judy Hanwen Shen, Craig Ferguson, Agata Lapedriza, Noah Jones, Shixiang Gu, Rosalind Picard Sequence Tutor: Conservative Fine-Tuning of Sequence Generation Models with KL-control Natasha Jaques, Shixiang Gu, Dzmitry Bahdanau, José Miguel Hernández-Lobato, Richard E. Turner, Douglas Eck PsiPhi-Learning: Reinforcement Learning with Demonstrations using Successor Features and Inverse Temporal Difference Learning Angelos Filos, Clare Lyle, Yarin Gal, Sergey Levine, Natasha Jaques, Gregory Farquhar Basis for Intentions: Efficient Inverse Reinforcement Learning using Past Experience Marwa Abdulhai, Natasha Jaques, Sergey Levine Additional References Fine-Tuning Language Models from Human Preferences, Daniel M. Ziegler et al 2019 Learning to summarize from human feedback, Nisan Stiennon et al 2020 Training language models to follow instructions with human feedback, Long Ouyang et al 2022
	Jacob Beck and Risto Vuorio	07 Mar 2023	01:07:05
Jacob Beck and Risto Vuorio on their recent Survey of Meta-Reinforcement Learning. Jacob and Risto are Ph.D. students at Whiteson Research Lab at University of Oxford. Featured Reference A Survey of Meta-Reinforcement Learning Jacob Beck, Risto Vuorio, Evan Zheran Liu, Zheng Xiong, Luisa Zintgraf, Chelsea Finn, Shimon Whiteson Additional References VariBAD: A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning, Luisa Zintgraf et al Mastering Diverse Domains through World Models (Dreamerv3), Hafner et al Unsupervised Meta-Learning for Reinforcement Learning (MAML), Gupta et al Decoupling Exploration and Exploitation for Meta-Reinforcement Learning without Sacrifices (DREAM), Liu et al RL2: Fast Reinforcement Learning via Slow Reinforcement Learning, Duan et al Learning to reinforcement learn, Wang et al
	John Schulman	18 Oct 2022	00:44:21
John Schulman is a cofounder of OpenAI, and currently a researcher and engineer at OpenAI. Featured References WebGPT: Browser-assisted question-answering with human feedback Reiichiro Nakano, Jacob Hilton, Suchir Balaji, Jeff Wu, Long Ouyang, Christina Kim, Christopher Hesse, Shantanu Jain, Vineet Kosaraju, William Saunders, Xu Jiang, Karl Cobbe, Tyna Eloundou, Gretchen Krueger, Kevin Button, Matthew Knight, Benjamin Chess, John Schulman Training language models to follow instructions with human feedback Long Ouyang, Jeff Wu, Xu Jiang, Diogo Almeida, Carroll L. Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul Christiano, Jan Leike, Ryan Lowe Additional References Our approach to alignment research, OpenAI 2022 Training Verifiers to Solve Math Word Problems, Cobbe et al 2021 UC Berkeley Deep RL Bootcamp Lecture 6: Nuts and Bolts of Deep RL Experimentation, John Schulman 2017 Proximal Policy Optimization Algorithms, Schulman 2017 Optimizing Expectations: From Deep Reinforcement Learning to Stochastic Computation Graphs, Schulman 2016
	Sven Mika	19 Aug 2022	00:34:56
Sven Mika is the Reinforcement Learning Team Lead at Anyscale, and lead committer of RLlib. He holds a PhD in biomathematics, bioinformatics, and computational biology from Witten/Herdecke University. Featured References RLlib Documentation: RLlib: Industry-Grade Reinforcement Learning Ray: Documentation RLlib: Abstractions for Distributed Reinforcement Learning Eric Liang, Richard Liaw, Philipp Moritz, Robert Nishihara, Roy Fox, Ken Goldberg, Joseph E. Gonzalez, Michael I. Jordan, Ion Stoica Episode sponsor: Anyscale Ray 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.
	Karol Hausman and Fei Xia	16 Aug 2022	01:03:09
Karol Hausman is a Senior Research Scientist at Google Brain and an Adjunct Professor at Stanford working on robotics and machine learning. Karol is interested in enabling robots to acquire general-purpose skills with minimal supervision in real-world environments. Fei Xia is a Research Scientist with Google Research. Fei Xia is mostly interested in robot learning in complex and unstructured environments. Previously he has been approaching this problem by learning in realistic and scalable simulation environments (GibsonEnv, iGibson). Most recently, he has been exploring using foundation models for those challenges. Featured References Do As I Can, Not As I Say: Grounding Language in Robotic Affordances [ website ] Michael Ahn, Anthony Brohan, Noah Brown, Yevgen Chebotar, Omar Cortes, Byron David, Chelsea Finn, Keerthana Gopalakrishnan, Karol Hausman, Alex Herzog, Daniel Ho, Jasmine Hsu, Julian Ibarz, Brian Ichter, Alex Irpan, Eric Jang, Rosario Jauregui Ruano, Kyle Jeffrey, Sally Jesmonth, Nikhil J Joshi, Ryan Julian, Dmitry Kalashnikov, Yuheng Kuang, Kuang-Huei Lee, Sergey Levine, Yao Lu, Linda Luu, Carolina Parada, Peter Pastor, Jornell Quiambao, Kanishka Rao, Jarek Rettinghouse, Diego Reyes, Pierre Sermanet, Nicolas Sievers, Clayton Tan, Alexander Toshev, Vincent Vanhoucke, Fei Xia, Ted Xiao, Peng Xu, Sichun Xu, Mengyuan Yan Inner Monologue: Embodied Reasoning through Planning with Language Models Wenlong Huang, Fei Xia, Ted Xiao, Harris Chan, Jacky Liang, Pete Florence, Andy Zeng, Jonathan Tompson, Igor Mordatch, Yevgen Chebotar, Pierre Sermanet, Noah Brown, Tomas Jackson, Linda Luu, Sergey Levine, Karol Hausman, Brian Ichter Additional References Large-scale simulation for embodied perception and robot learning, Xia 2021 QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation, Kalashnikov et al 2018 MT-Opt: Continuous Multi-Task Robotic Reinforcement Learning at Scale, Kalashnikov et al 2021 ReLMoGen: Leveraging Motion Generation in Reinforcement Learning for Mobile Manipulation, Xia et al 2020 Actionable Models: Unsupervised Offline Reinforcement Learning of Robotic Skills, Chebotar et al 2021 Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language, Zeng et al 2022 Episode sponsor: Anyscale Ray 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.
	Sai Krishna Gottipati	01 Aug 2022	01:08:11
Saikrishna Gottipati is an RL Researcher at AI Redefined, working on RL, MARL, human in the loop learning. Featured References Cogment: Open Source Framework For Distributed Multi-actor Training, Deployment & Operations AI Redefined, Sai Krishna Gottipati, Sagar Kurandwad, Clodéric Mars, Gregory Szriftgiser, François Chabot Do As You Teach: A Multi-Teacher Approach to Self-Play in Deep Reinforcement Learning Currently under review Learning to navigate the synthetically accessible chemical space using reinforcement learning Sai 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 Bengio Additional References Asymmetric 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 al Episode sponsor: Anyscale Ray 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 2	09 May 2022	00:58:33
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	12 Apr 2022	01:37:04
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
	Jordan Terry	22 Feb 2022	01:03:48
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 References PettingZoo: Gym for Multi-Agent Reinforcement Learning J. 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 Ravi PettingZoo on Github gym on Github Additional References Time Limits in Reinforcement Learning, Pardo et al 2017 Deep Reinforcement Learning at the Edge of the Statistical Precipice, Agarwal et al 2021
	RLC 2024 - Posters and Hallways 3	18 Sep 2024	00:06:43
Posters and Hallway episodes are short interviews and poster summaries. Recorded at RLC 2024 in Amherst MA. Featuring: 0:01 Kris De Asis from Openmind on Time Discretization 2:23 Anna Hakhverdyan from U of Alberta on Online Hyperparameters 3:59 Dilip Arumugam from Princeton on Information Theory and Exploration 5:04 Micah Carroll from UC Berkeley on Changing preferences and AI alignment
	Robert Lange	20 Dec 2021	01:10:57
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	18 Nov 2021	00:24:07
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	27 Sep 2021	01:09:35
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	30 Aug 2021	00:41:30
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	18 Aug 2021	01:06:02
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	20 Jul 2021	01:31:36
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	06 Jul 2021	00:54:30
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	21 Jun 2021	01:40:35
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	17 May 2021	01:12:14
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
	Marc G. Bellemare	13 May 2021	00:57:40
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 Kavukcuoglu, David Silver, Andrei A. Rusu, Joel Veness, Marc G. Bellemare, Alex Graves, Martin Riedmiller, Andreas K. Fidjeland, Georg Ostrovski, Stig Petersen, Charles Beattie, Amir Sadik, Ioannis Antonoglou, Helen King, Dharshan Kumaran, Daan Wierstra, Shane Legg & Demis Hassabis Autonomous navigation of stratospheric balloons using reinforcement learning Marc G. Bellemare, Salvatore Candido, Pablo Samuel Castro, Jun Gong, Marlos C. Machado, Subhodeep Moitra, Sameera S. Ponda & Ziyu Wang Additional References CAIDA Talk: A tour of distributional reinforcement learning November 18, 2020 - Marc G. Bellemare Amii AI Seminar Series: Autonomous nav of stratospheric balloons using RL, Marlos C. Machado UMD RLSS \| Marc Bellemare \| A History of Reinforcement Learning: Atari to Stratospheric Balloons TalkRL: Marlos C. Machado, Dr. Machado also spoke to us about various aspects of ALE and Project Loon in depth Hyperbolic discounting and learning over multiple horizons, Fedus et al 2019 Marc G. Bellemare on Twitter
	RLC 2024 - Posters and Hallways 2	16 Sep 2024	00:15:52
Posters and Hallway episodes are short interviews and poster summaries. Recorded at RLC 2024 in Amherst MA. Featuring: 0:01 Hector Kohler from Centre Inria de l'Université de Lille with "Interpretable and Editable Programmatic Tree Policies for Reinforcement Learning" 2:29 Quentin Delfosse from TU Darmstadt on "Interpretable Concept Bottlenecks to Align Reinforcement Learning Agents" 4:15 Sonja Johnson-Yu from Harvard on "Understanding biological active sensing behaviors by interpreting learned artificial agent policies" 6:42 Jannis Blüml from TU Darmstadt on "OCAtari: Object-Centric Atari 2600 Reinforcement Learning Environments" 8:20 Cameron Allen from UC Berkeley on "Resolving Partial Observability in Decision Processes via the Lambda Discrepancy" 9:48 James Staley from Tufts on "Agent-Centric Human Demonstrations Train World Models" 14:54 Jonathan Li from Rensselaer Polytechnic Institute
	Robert Osazuwa Ness	08 May 2021	01:18:43
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, Free course Robert Osazuwa Ness on Google Scholar Gamalon Inc Causal Reinforcement Learning talks, Elias Bareinboim The Bitter Lesson, Rich Sutton 2019 The Need for Biases in Learning Generalizations, Tom Mitchell 1980 Schema Networks: Zero-shot Transfer with a Generative Causal Model of Intuitive Physics, Kansky et al 2017
	Marlos C. Machado	12 Apr 2021	01:31:31
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. Hausknecht, Michael Bowling Contrastive Behavioral Similarity Embeddings for Generalization in Reinforcement Learning [ video ] Rishabh Agarwal, Marlos C. Machado, Pablo Samuel Castro, Marc G. Bellemare Efficient Exploration in Reinforcement Learning through Time-Based Representations Marlos C. Machado A Laplacian Framework for Option Discovery in Reinforcement Learning [ video ] Marlos C. Machado, Marc G. Bellemare, Michael H. Bowling Eigenoption Discovery through the Deep Successor Representation Marlos C. Machado, Clemens Rosenbaum, Xiaoxiao Guo, Miao Liu, Gerald Tesauro, Murray Campbell Exploration in Reinforcement Learning with Deep Covering Options Yuu Jinnai, Jee Won Park, Marlos C. Machado, George Dimitri Konidaris Autonomous navigation of stratospheric balloons using reinforcement learning Marc G. Bellemare, Salvatore Candido, Pablo Samuel Castro, Jun Gong, Marlos C. Machado, Subhodeep Moitra, Sameera S. Ponda & Ziyu Wang Generalization and Regularization in DQN Jesse Farebrother, Marlos C. Machado, Michael Bowling Additional References Amii AI Seminar Series: Marlos C. Machado - Autonomous navigation of stratospheric balloons using RL State of the Art Control of Atari Games Using Shallow Reinforcement Learning, Liang et al Introspective Agents: Confidence Measures for General Value Functions, Sherstan et al
	Nathan Lambert	22 Mar 2021	00:50:35
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 Model-Based Reinforcement Learning Nathan O. Lambert, Daniel S. Drew, Joseph Yaconelli, Roberto Calandra, Sergey Levine, Kristofer S.J. Pister On the Importance of Hyperparameter Optimization for Model-based Reinforcement Learning Baohe Zhang, Raghu Rajan, Luis Pineda, Nathan Lambert, André Biedenkapp, Kurtland Chua, Frank Hutter, Roberto Calandra Additional References Nathan Lambert's blog Nathan Lambert on Google scholar
	Kai Arulkumaran	16 Mar 2021	00:46:26
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 Reinforcement Learning Rupesh Kumar Srivastava, Pranav Shyam, Filipe Mutz, Wojciech Jaśkowski, Jürgen Schmidhuber Additional References Araya NNAISENSE Kai Arulkumaran on Google Scholar https://github.com/Kaixhin/rlenvs https://github.com/Kaixhin/Atari https://github.com/Kaixhin/Rainbow Tschiatschek, S., Arulkumaran, K., Stühmer, J. & Hofmann, K. (2018). Variational Inference for Data-Efficient Model Learning in POMDPs. arXiv:1805.09281. Arulkumaran, K., Dilokthanakul, N., Shanahan, M. & Bharath, A. A. (2016). Classifying Options for Deep Reinforcement Learning. International Joint Conference on Artificial Intelligence, Deep Reinforcement Learning Workshop. Garnelo, M., Arulkumaran, K. & Shanahan, M. (2016). Towards Deep Symbolic Reinforcement Learning. Annual Conference on Neural Information Processing Systems, Deep Reinforcement Learning Workshop. Arulkumaran, K., Deisenroth, M. P., Brundage, M. & Bharath, A. A. (2017). Deep reinforcement learning: A brief survey. IEEE Signal Processing Magazine. Agostinelli, A., Arulkumaran, K., Sarrico, M., Richemond, P. & Bharath, A. A. (2019). Memory-Efficient Episodic Control Reinforcement Learning with Dynamic Online k-means. Annual Conference on Neural Information Processing Systems, Workshop on Biological and Artificial Reinforcement Learning. Sarrico, M., Arulkumaran, K., Agostinelli, A., Richemond, P. & Bharath, A. A. (2019). Sample-Efficient Reinforcement Learning with Maximum Entropy Mellowmax Episodic Control. Annual Conference on Neural Information Processing Systems, Workshop on Biological and Artificial Reinforcement Learning.
	Michael Dennis	26 Jan 2021	01:00:50
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 Environment Design [PAIRED] Michael Dennis, Natasha Jaques, Eugene Vinitsky, Alexandre Bayen, Stuart Russell, Andrew Critch, Sergey Levine Videos Adversarial Policies: Attacking Deep Reinforcement Learning Adam Gleave, Michael Dennis, Cody Wild, Neel Kant, Sergey Levine, Stuart Russell Homepage and Videos Accumulating Risk Capital Through Investing in Cooperation Charlotte Roman, Michael Dennis, Andrew Critch, Stuart Russell Quantifying Differences in Reward Functions [EPIC] Adam Gleave, Michael Dennis, Shane Legg, Stuart Russell, Jan Leike Additional References Safe Opponent Exploitation, Sam Ganzfried And Tuomas Sandholm 2015 Social Influence as Intrinsic Motivation for Multi-Agent Deep Reinforcement Learning, Natasha Jaques et al 2019 Autocurricula and the Emergence of Innovation from Social Interaction: A Manifesto for Multi-Agent Intelligence Research, Leibo et al 2019 Leveraging Procedural Generation to Benchmark Reinforcement Learning, Karl Cobbe et al 2019 Paired Open-Ended Trailblazer (POET): Endlessly Generating Increasingly Complex and Diverse Learning Environments and Their Solutions, Wang et al 2019 Consequences of Misaligned AI, Zhuang et al 2020 Conservative Agency via Attainable Utility Preservation, Turner et al 2019
	Roman Ring	11 Jan 2021	00:42:23
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 Reinforcement Learning, Vinyals et al 2017 Safe and Efficient Off-Policy Reinforcement Learning [Retrace(λ)], Munos et al 2016 Sample Efficient Actor-Critic with Experience Replay [ACER], Wang et al 2016 IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures [IMPALA/V-trace], Espeholt et al 2018
	Shimon Whiteson	06 Dec 2020	00:53:35
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 Factorisation for Deep Multi-Agent Reinforcement Learning Tabish Rashid, Mikayel Samvelyan, Christian Schroeder de Witt, Gregory Farquhar, Jakob Foerster, Shimon Whiteson Additional References Shimon Whiteson - Multi-agent RL, MIT Embodied Intelligence Seminar The StarCraft Multi-Agent Challenge, Samvelyan et al 2019 Direct Policy Transfer with Hidden Parameter Markov Decision Processes, Yao et al 2018 Value-Decomposition Networks For Cooperative Multi-Agent Learning, Sunehag et al 2017 Whiteson Research Lab Waymo acquires Latent Logic to accelerate progress towards safe, driverless vehicles, Oxford News Waymo
	Aravind Srinivas	21 Sep 2020	01:25:27
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 Unsupervised Representations for Reinforcement Learning Aravind Srinivas, Michael Laskin, Pieter Abbeel Reinforcement Learning with Augmented Data Michael Laskin, Kimin Lee, Adam Stooke, Lerrel Pinto, Pieter Abbeel, Aravind Srinivas SUNRISE: A Simple Unified Framework for Ensemble Learning in Deep Reinforcement Learning Kimin Lee, Michael Laskin, Aravind Srinivas, Pieter Abbeel Additional References CS294-158-SP20 Deep Unsupervised Learning, Berkeley Phasic Policy Gradient, Karl Cobbe, Jacob Hilton, Oleg Klimov, John Schulman Bootstrap your own latent: A new approach to self-supervised Learning , Grill et al 2020
	Taylor Killian	17 Aug 2020	01:29:55
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 Across Patient Variations with Hidden Parameter Markov Decision Processes Killian, Konidaris, Doshi-Velez Counterfactually Guided Policy Transfer in Clinical Settings Killian, Ghassemi, Joshi Additional References Hidden Parameter Markov Decision Processes: A Semiparametric Regression Approach for Discovering Latent Task Parametrizations, Doshi-Velez, Konidaris Mimic III, a freely accessible critical care database. Johnson AEW, Pollard TJ, Shen L, Lehman L, Feng M, Ghassemi M, Moody B, Szolovits P, Celi LA, and Mark RG The Artificial Intelligence Clinician learns optimal treatment strategies for sepsis in intensive care, Komorowski et al
	Nan Jiang	06 Jul 2020	01:11:46
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 Model-free Approaches Wen Sun, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford Information-Theoretic Considerations in Batch Reinforcement Learning Jinglin Chen, Nan Jiang Additional References Towards a Unified Theory of State Abstraction for MDPs, Lihong Li, Thomas J. Walsh, Michael L. Littman Doubly Robust Off-policy Value Evaluation for Reinforcement Learning, Nan Jiang, Lihong Li Minimax Confidence Interval for Off-Policy Evaluation and Policy Optimization, Nan Jiang, Jiawei Huang Empirical Study of Off-Policy Policy Evaluation for Reinforcement Learning, Cameron Voloshin, Hoang M. Le, Nan Jiang, Yisong Yue Errata [Robin] I misspoke when I said in domain randomization we want the agent to "ignore" domain parameters. What I should have said is, we want the agent to perform well within some range of domain parameters, it should be robust with respect to domain parameters.
	RLC 2024 - Posters and Hallways 1	10 Sep 2024	00:05:46
Posters and Hallway episodes are short interviews and poster summaries. Recorded at RLC 2024 in Amherst MA. Featuring: 0:01 Ann Huang from Harvard on Learning Dynamics and the Geometry of Neural Dynamics in Recurrent Neural Controllers 1:37 Jannis Blüml from TU Darmstadt on HackAtari: Atari Learning Environments for Robust and Continual Reinforcement Learning 3:13 Benjamin Fuhrer from NVIDIA on Gradient Boosting Reinforcement Learning 3:54 Paul Festor from Imperial College London on Evaluating the impact of explainable RL on physician decision-making in high-fidelity simulations: insights from eye-tracking metrics
	Danijar Hafner	14 May 2020	02:00:29
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, Rui Ponte Costa, Archy de Berker, Surya Ganguli, Colleen J. Gillon , Danijar Hafner, Adam Kepecs, Nikolaus Kriegeskorte, Peter Latham , Grace W. Lindsay, Kenneth D. Miller , Richard Naud , Christopher C. Pack, Panayiota Poirazi , Pieter Roelfsema , João Sacramento, Andrew Saxe, Benjamin Scellier, Anna C. Schapiro , Walter Senn, Greg Wayne, Daniel Yamins, Friedemann Zenke, Joel Zylberberg, Denis Therien, Konrad P. Kording Learning Latent Dynamics for Planning from Pixels Danijar Hafner, Timothy Lillicrap, Ian Fischer, Ruben Villegas, David Ha, Honglak Lee, James Davidson Dream to Control: Learning Behaviors by Latent Imagination Danijar Hafner, Timothy Lillicrap, Jimmy Ba, Mohammad Norouzi Planning to Explore via Self-Supervised World Models Ramanan Sekar, Oleh Rybkin, Kostas Daniilidis, Pieter Abbeel, Danijar Hafner, Deepak Pathak Additional References Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model Schrittwieser et al Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm Silver et al Shaping Belief States with Generative Environment Models for RL Gregor et al Model-Based Active Exploration Shyam et al Errata [Robin] Around 1:37 I say "some ... world models get confused by random noise". I meant "some curiosity formulations", not "world models"
	Csaba Szepesvari	05 Apr 2020	00:48:42
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 Szepesvári Bandit Algorithms, Tor Lattimore, Csaba Szepesvári Algorithms for Reinforcement Learning, Csaba Szepesvári The Predictron: End-To-End Learning and Planning, David Silver, Hado van Hasselt, Matteo Hessel, Tom Schaul, Arthur Guez, Tim Harley, Gabriel Dulac-Arnold, David Reichert, Neil Rabinowitz, Andre Barreto, Thomas Degris A Bayesian framework for reinforcement learning, Strens Solving Rubik’s Cube with a Robot Hand ; Paper, OpenAI, Ilge Akkaya, Marcin Andrychowicz, Maciek Chociej, Mateusz Litwin, Bob McGrew, Arthur Petron, Alex Paino, Matthias Plappert, Glenn Powell, Raphael Ribas, Jonas Schneider, Nikolas Tezak, Jerry Tworek, Peter Welinder, Lilian Weng, Qiming Yuan, Wojciech Zaremba, Lei Zhang The Nonstochastic Multiarmed Bandit Problem, Peter Auer, Nicolò Cesa-Bianchi, Yoav Freund, and Robert E. Schapire Deep Learning with Bayesian Principles, Mohammad Emtiyaz Khan 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
	Ben Eysenbach	30 Mar 2020	00:49:18
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 Levine Search on the Replay Buffer: Bridging Planning and Reinforcement Learning Benjamin Eysenbach, Ruslan Salakhutdinov, Sergey Levine Additional References Behaviour Suite for Reinforcement Learning, Ian Osband, Yotam Doron, Matteo Hessel, John Aslanides, Eren Sezener, Andre Saraiva, Katrina McKinney, Tor Lattimore, Csaba Szepesvari, Satinder Singh, Benjamin Van Roy, Richard Sutton, David Silver, Hado Van Hasselt Learning Latent Plans from Play, Corey Lynch, Mohi Khansari, Ted Xiao, Vikash Kumar, Jonathan Tompson, Sergey Levine, Pierre Sermanet Finale Doshi-Velez Emma Brunskill Closed-loop optimization of fast-charging protocols for batteries with machine learning, Peter Attia, Aditya Grover, Norman Jin, Kristen Severson, Todor Markov, Yang-Hung Liao, Michael Chen, Bryan Cheong, Nicholas Perkins, Zi Yang, Patrick Herring, Muratahan Aykol, Stephen Harris, Richard Braatz, Stefano Ermon, William Chueh CMU 10-703 Deep Reinforcement Learning, Fall 2019, Carnegie Mellon University ICML Exploration in Reinforcement Learning workshop
	NeurIPS 2019 Deep RL Workshop	20 Dec 2019	00:56:18
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 (University of Liege); Gilles Louppe (University of Liège); Pierre Geurts (University of Liège); Marco Wiering (University of Groningen) [external pdf link] 4:16 Single Deep Counterfactual Regret Minimization; Eric Steinberger (University of Cambridge). 5:38 On the Convergence of Episodic Reinforcement Learning Algorithms at the Example of RUDDER; Markus Holzleitner (LIT AI Lab, Institute for Machine Learning, Johannes Kepler University Linz, Austria); José Arjona-Medina (LIT AI Lab, Institute for Machine Learning, Johannes Kepler University Linz, Austria); Marius-Constantin Dinu (LIT AI Lab / University Linz ); Sepp Hochreiter (LIT AI Lab, Institute for Machine Learning, Johannes Kepler University Linz, Austria). 9:33 Objective Mismatch in Model-based Reinforcement Learning; Nathan Lambert (UC Berkeley); Brandon Amos (Facebook); Omry Yadan (Facebook); Roberto Calandra (Facebook). 10:51 Option Discovery using Deep Skill Chaining; Akhil Bagaria (Brown University); George Konidaris (Brown University). 13:44 Blue River Controls: A toolkit for Reinforcement Learning Control Systems on Hardware; Kirill Polzounov (University of Calgary); Ramitha Sundar (Blue River Technology); Lee Reden (Blue River Technology). 14:52 LeDeepChef: Deep Reinforcement Learning Agent for Families of Text-Based Games; Leonard Adolphs (ETHZ); Thomas Hofmann (ETH Zurich). 16:30 Accelerating Training in Pommerman with Imitation and Reinforcement Learning; Hardik Meisheri (TCS Research); Omkar Shelke (TCS Research); Richa Verma (TCS Research); Harshad Khadilkar (TCS Research). 17:27 Dream to Control: Learning Behaviors by Latent Imagination; Danijar Hafner (Google); Timothy Lillicrap (DeepMind); Jimmy Ba (University of Toronto); Mohammad Norouzi (Google Brain) [external pdf link]. 20:48 Adaptive Temperature Tuning for Mellowmax in Deep Reinforcement Learning; Seungchan Kim (Brown University); George Konidaris (Brown). 22:05 Meta-learning curiosity algorithms; Ferran Alet (MIT); Martin Schneider (MIT); Tomas Lozano-Perez (MIT); Leslie Kaelbling (MIT). 24:09 Predictive Coding for Boosting Deep Reinforcement Learning with Sparse Rewards; Xingyu Lu (Berkeley); Stas Tiomkin (BAIR, UC Berkeley); Pieter Abbeel (UC Berkeley). 25:44 Swarm-inspired Reinforcement Learning via Collaborative Inter-agent Knowledge Distillation; Zhang-Wei Hong (Preferred Networks); Prabhat Nagarajan (Preferred Networks); Guilherme Maeda (Preferred Networks). 26:35 Multiplayer AlphaZero; Nicholas Petosa (Georgia Institute of Technology); Tucker Balch (Ga Tech) [external pdf link]. 27:43 Prioritized Sequence Experience Replay; Marc Brittain (Iowa State University); Joshua Bertram (Iowa State University); Xuxi Yang (Iowa State University); Peng Wei (Iowa State University) [external pdf link]. 29:14 Recurrent neural-linear posterior sampling for non-stationary bandits; Paulo Rauber (IDSIA); Aditya Ramesh (USI); Jürgen Schmidhuber (IDSIA - Lugano). 29:36 Improving Evolutionary Strategies With Past Descent Directions; Asier Mujika (ETH Zurich); Florian Meier (ETH Zurich); Marcelo Matheus Gauy (ETH Zurich); Angelika Steger (ETH Zurich) [external pdf link]. 31:40 ZPD Teaching Strategies for Deep Reinforcement Learning from Demonstrations; Daniel Seita (University of California, Berkeley); David Chan (University of California, Berkeley); Roshan Rao (UC Berkeley); Chen Tang (UC Berkeley); Mandi Zhao (UC Berkeley); John Canny (UC Berkeley) [external pdf link]. 33:05 Bottom-Up Meta-Policy Search; Luckeciano Melo (Aeronautics Institute of Technology); Marcos Máximo (Aeronautics Institute of Technology); Adilson Cunha (Aeronautics Institute of Technology) [external pdf link]. 33:37 MERL: Multi-Head Reinforcement Learning; Yannis Flet-Berliac (University of Lille / Inria); Philippe Preux (INRIA) [external pdf link]. 35:30 Emergen...

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