TalkRL: The Reinforcement Learning Podcast: Recent Episodes
Robin Ranjit Singh Chauhan
TalkRL podcast is All Reinforcement Learning, All the Time. In-depth interviews with brilliant people at the forefront of RL research and practice. Guests from places like MILA, MIT, DeepMind, Berkeley, Amii, Oxford, Google Research, Brown, Waymo, Caltech, and Vector Institute. Hosted by Robin Ranjit Singh Chauhan. Technical content.
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 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 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
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 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 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
SayCan: 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
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 & OperationsAI 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 LearningCurrently under review
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 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
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 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
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
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 Learning
Xianyuan Zhan, Haoran Xu, Yue Zhang, Yusen Huo, Xiangyu Zhu, Honglei Yin, Yu Zheng
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
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
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
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
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 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 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 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 CooperationCharlotte 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 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 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 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 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 ProcessesYao, Killian, Konidaris, Doshi-Velez
Robust and Efficient Transfer Learning with Hidden Parameter Markov Decision ProcessesKillian, Daulton, Konidaris, Doshi-Velez
Transfer Learning Across Patient Variations with Hidden Parameter Markov Decision ProcessesKillian, Konidaris, Doshi-Velez
Counterfactually Guided Policy Transfer in Clinical SettingsKillian, 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 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 AlgorithmsAlekh Agarwal Nan Jiang Sham M. Kakade
- Model-based RL in Contextual Decision Processes: PAC bounds and Exponential Improvements over Model-free ApproachesWen Sun, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford
- Information-Theoretic Considerations in Batch Reinforcement LearningJinglin 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.
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 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 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
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 Emergent Tool Use from Multi-Agent Autocurricula; Bowen Baker (OpenAI); Ingmar Kanitscheider (OpenAI); Todor Markov (OpenAI); Yi Wu (UC Berkeley); Glenn Powell (OpenAI); Bob McGrew (OpenAI); Igor Mordatch ().
- 37:09 Learning an off-policy predictive state representation for deep reinforcement learning for vision-based steering in autonomous driving; Daniel Graves (Huawei)
- 39:37 Multi-Task Reinforcement Learning without Interference; Tianhe Yu (Stanford University); Saurabh Kumar (Stanford); Abhishek Gupta (UC Berkeley); Karol Hausman (Google Brain); Sergey Levine (UC Berkeley); Chelsea Finn (UC Berkeley).
- 40:52 Behavior-Regularized Offline Reinforcement Learning; Yifan Wu (Carnegie Mellon University); George Tucker (Google Brain); Ofir Nachum (Google) [external pdf link].
- 42:36 If MaxEnt RL is the Answer, What is the Question?; Ben Eysenbach (Carnegie Mellon University); Sergey Levine (UC Berkeley) [external pdf link].
- 43:30 Receiving Uncertainty-Aware Advice in Deep Reinforcement Learning; Felipe Leno da Silva (University of Sao Paulo); Pablo Hernandez-Leal (Borealis AI); Bilal Kartal (Borealis AI); Matthew Taylor (Borealis AI).
- 45:03 Striving for Simplicity in Off-Policy Deep Reinforcement Learning; Rishabh Agarwal (Google Research, Brain Team); Dale Schuurmans (Google / University of Alberta); Mohammad Norouzi (Google Brain) [external pdf link].
- 45:32 Interactive Fiction Games: A Colossal Adventure; Matthew Hausknecht (Microsoft Research); Prithviraj Ammanabrolu (Georgia Institute of Technology); Marc-Alexandre Côté (Microsoft Research); Xingdi Yuan (Microsoft Research) [external pdf link].
- 52:20 rlpyt: A Research Code Base for Deep Reinforcement Learning in PyTorch; Adam Stooke (UC Berkeley); Pieter Abbeel (UC Berkeley) [ Repo: https://github.com/astooke/rlpyt ]
- 53:49 Learning to Drive using Waypoints; Tanmay Agarwal, Hitesh Arora, Tanvir Parhar, Shubhankar V Deshpande, Jeff Schneider - from the NeurIPS 2019 Workshop on Machine Learning for Autonomous Driving Workshop
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 Precup
Benchmarking Batch Deep Reinforcement Learning Algorithms
Scott Fujimoto, Edoardo Conti, Mohammad Ghavamzadeh, Joelle Pineau
Additional References
- Striving for Simplicity in Off-Policy Deep Reinforcement Learning
Rishabh Agarwal, Dale Schuurmans, Mohammad Norouzi - Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, Sergey Levine - 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 - Continuous control with deep reinforcement learning
Timothy P. Lillicrap, Jonathan J. Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, Daan Wierstra - Distributed Distributional Deterministic Policy Gradients
Gabriel Barth-Maron, Matthew W. Hoffman, David Budden, Will Dabney, Dan Horgan, Dhruva TB, Alistair Muldal, Nicolas Heess, Timothy Lillicrap
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 References
- Metacontrol for Adaptive Imagination-Based Optimization
Jessica B. Hamrick, Andrew J. Ballard, Razvan Pascanu, Oriol Vinyals, Nicolas Heess, Peter W. Battaglia - Surprising Negative Results for Generative Adversarial Tree Search
Kamyar Azizzadenesheli, Brandon Yang, Weitang Liu, Zachary C Lipton, Animashree Anandkumar - Metareasoning and Mental Simulation
Jessica B. Hamrick - Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew Lai, Arthur Guez, Marc Lanctot, Laurent Sifre, Dharshan Kumaran, Thore Graepel, Timothy Lillicrap, Karen Simonyan, Demis Hassabis - Object-oriented state editing for HRL
Victor Bapst, Alvaro Sanchez-Gonzalez, Omar Shams, Kimberly Stachenfeld, Peter W. Battaglia, Satinder Singh, Jessica B. Hamrick - FeUdal Networks for Hierarchical Reinforcement Learning
Alexander Sasha Vezhnevets, Simon Osindero, Tom Schaul, Nicolas Heess, Max Jaderberg, David Silver, Koray Kavukcuoglu - PILCO: A Model-Based and Data-Efficient Approach to Policy Search
Marc Peter Deisenroth, Carl Edward Rasmussen - Blueberry Earth
Anders Sandberg
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, Robert Dadashi, Adrien Ali Taiga, Pablo Samuel Castro, Nicolas Le Roux, Dale Schuurmans, Tor Lattimore, Clare Lyle
Dopamine: A Research Framework for Deep Reinforcement Learning
Pablo Samuel Castro, Subhodeep Moitra, Carles Gelada, Saurabh Kumar, Marc G. Bellemare
Dopamine RL framework on github
Tensorflow Agents on github
Additional References
- Using Linear Programming for Bayesian Exploration in Markov Decision Processes
Pablo Samuel Castro, Doina Precup - Using bisimulation for policy transfer in MDPs
Pablo Samuel Castro, Doina Precup - Rainbow: Combining Improvements in Deep Reinforcement Learning
Matteo Hessel, Joseph Modayil, Hado van Hasselt, Tom Schaul, Georg Ostrovski, Will Dabney, Dan Horgan, Bilal Piot, Mohammad Azar, David Silver - Implicit Quantile Networks for Distributional Reinforcement Learning
Will Dabney, Georg Ostrovski, David Silver, Rémi Munos - A Distributional Perspective on Reinforcement Learning
Marc G. Bellemare, Will Dabney, Rémi Munos
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 Anandkumar
Surprising Negative Results for Generative Adversarial Tree Search
Kamyar Azizzadenesheli, Brandon Yang, Weitang Liu, Zachary C Lipton, Animashree Anandkumar
Maybe a few considerations in Reinforcement Learning Research?
Kamyar Azizzadenesheli
Additional References
- Model-Based Reinforcement Learning for Atari
Lukasz Kaiser, Mohammad Babaeizadeh, Piotr Milos, Blazej Osinski, Roy H Campbell, Konrad Czechowski, Dumitru Erhan, Chelsea Finn, Piotr Kozakowski, Sergey Levine, Afroz Mohiuddin, Ryan Sepassi, George Tucker, Henryk Michalewski - Near-optimal Regret Bounds for Reinforcement Learning
Thomas Jaksch, Ronald Ortner, Peter Auer - Curious Model-Building Control Systems
Jürgen Schmidhuber - Rainbow: Combining Improvements in Deep Reinforcement Learning
Matteo Hessel, Joseph Modayil, Hado van Hasselt, Tom Schaul, Georg Ostrovski, Will Dabney, Dan Horgan, Bilal Piot, Mohammad Azar, David Silver - Schema Networks: Zero-shot Transfer with a Generative Causal Model of Intuitive Physics
Ken Kansky, Tom Silver, David A. Mély, Mohamed Eldawy, Miguel Lázaro-Gredilla, Xinghua Lou, Nimrod Dorfman, Szymon Sidor, Scott Phoenix, Dileep George - Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew Lai, Arthur Guez, Marc Lanctot, Laurent Sifre, Dharshan Kumaran, Thore Graepel, Timothy Lillicrap, Karen Simonyan, Demis Hassabis
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 gain tuning.
He works mainly with neuroevolution, genetic algorithms, and of course reinforcement learning, applied to mobile robots. He holds a masters degree in Machine learning, and a bachelors in Computer science from the Université Paris-Saclay.
Featured References
stable-baselines on github
Ashley Hill, Antonin Raffin primary authors.
S-RL Toolbox
Antonin Raffin, Ashley Hill, René Traoré, Timothée Lesort, Natalia Díaz-Rodríguez, David Filliat
Decoupling feature extraction from policy learning: assessing benefits of state representation learning in goal based robotics
Antonin Raffin, Ashley Hill, René Traoré, Timothée Lesort, Natalia Díaz-Rodríguez, David Filliat
Additional References
- Learning to Drive Smoothly in Minutes, Antonin Raffin
- Multimodal SRL (best paper at ICRA): Making Sense of Vision and Touch: Self-Supervised Learning of Multimodal Representations for Contact-Rich Tasks, Michelle A. Lee, Yuke Zhu, Krishnan Srinivasan, Parth Shah, Silvio Savarese, Li Fei-Fei, Animesh Garg, Jeannette Bohg
- Benchmarking Model-Based Reinforcement Learning, Tingwu Wang, Xuchan Bao, Ignasi Clavera, Jerrick Hoang, Yeming Wen, Eric Langlois, Shunshi Zhang, Guodong Zhang, Pieter Abbeel, Jimmy Ba
- TossingBot: Learning to Throw Arbitrary Objects with Residual Physics
Andy Zeng, Shuran Song, Johnny Lee, Alberto Rodriguez, Thomas Funkhouser - Stable Baselines roadmap
- OpenAI baselines stable-baselines github pull request
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 teach with rewards and punishments as communication, not reinforcements
Mark Ho, Fiery Cushman, Michael L. Littman, Joseph Austerweil
Theory of Minds: Understanding Behavior in Groups Through Inverse Planning
Michael Shum, Max Kleiman-Weiner, Michael L. Littman, Joshua B. Tenenbaum
Personalized education at scale
Saarinen, Cater, Littman
Additional References
- Michael Littman papers on Google Scholar, Semantic Scholar
- Reinforcement Learning on Udacity, Charles Isbell, Michael Littman, Chris Pryby
- Machine Learning on Udacity, Michael Littman, Charles Isbell, Pushkar Kolhe
- Temporal Difference Learning and TD-Gammon, Gerald Tesauro
- Playing Atari with Deep Reinforcement Learning, Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, Martin Riedmiller
- Ask Me Anything about MOOCs, D Fisher, C Isbell, ML Littman, M Wollowski, et al
- Reinforcement Learning and Decision Making (RLDM) Conference
- Algorithms for Sequential Decision Making, Michael Littman's Thesis
- Machine Learning A Cappella - Overfitting Thriller!, Michael Littman and Charles Isbell feat Infinite Harmony
- Turbotax Ad 2016: Genius Anna/Michael Littman
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 Multi-Agent Deep Reinforcement LearningNatasha Jaques, Angeliki Lazaridou, Edward Hughes, Caglar Gulcehre, Pedro A. Ortega, DJ Strouse, Joel Z. Leibo, Nando de Freitas
Tackling climate change with Machine LearningDavid 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
Additional References
- MIT Media Lab Flight Offsets, Caroline Jaffe, Juliana Cherston, Natasha Jaques
- Modeling Others using Oneself in Multi-Agent Reinforcement Learning,
Roberta Raileanu, Emily Denton, Arthur Szlam, Rob Fergus - Inequity aversion improves cooperation in intertemporal social dilemmas,
Edward Hughes, Joel Z. Leibo, Matthew G. Phillips, Karl Tuyls, Edgar A. Duéñez-Guzmán, Antonio García Castañeda, Iain Dunning, Tina Zhu, Kevin R. McKee, Raphael Koster, Heather Roff, Thore Graepel - Sequential Social Dilemma Games on github, Eugene Vinitsky, Natasha Jaques
- AI Alignment newsletter, Rohin Shah
- 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
- The social function of intellect, Nicholas Humphrey
- Autocurricula and the Emergence of Innovation from Social Interaction: A Manifesto for Multi-Agent Intelligence Research, Joel Z. Leibo, Edward Hughes, Marc Lanctot, Thore Graepel
- A Recipe for Training Neural Networks, Andrej Karpathy
- Emotionally Adaptive Intelligent Tutoring Systems using POMDPs, Natasha Jaques
- Sapiens, Yuval Noah Harari
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 wont stop to explain what a value function is, for example. Though we also wont assume everyone has read the very latest papers.
Why am I doing this? Because it’s a great way to learn from the most inspiring people in the field! There’s so much happening in the universe of RL, and there’s tons of interesting angles and so many fascinating minds to learn from.
Now I know there is no shortage of books, papers, and lectures, but so much goes unsaid.
I mean I guess if you work at MILA or AMII or Vector Institute, you might be having these conversations over coffee all the time, but I live in a little village in the woods in BC, so for me, these remote interviews are like a great way to have these conversations, and I hope sharing with the community makes it more worthwhile for everyone.
In terms of format, the first 2 episodes were interviews in longer form, around an hour long. Going forward, some may be a lot shorter, it depends on the guest.
If you want want to be a guest or suggest a guest, goto talkrl.com/about, you will find a link to a suggestion form.
Thanks for listening!