TalkRL: The Reinforcement Learning Podcast

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 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 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

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.

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 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. 

--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 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 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 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. 

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 [email protected] 

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...

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 Learning
Natasha Jaques, Angeliki Lazaridou, Edward Hughes, Caglar Gulcehre, Pedro A. Ortega, DJ Strouse, Joel Z. Leibo, Nando de Freitas

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 

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!