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Workshop
Deep Reinforcement Learning
Pieter Abbeel · Chelsea Finn · Joelle Pineau · David Silver · Satinder Singh · Joshua Achiam · Carlos Florensa · Christopher Grimm · Haoran Tang · Vivek Veeriah

Sat Dec 14 08:00 AM -- 07:00 PM (PST) @ West Exhibition Hall C
Event URL: https://sites.google.com/view/deep-rl-workshop-neurips-2019/home »

In recent years, the use of deep neural networks as function approximators has enabled researchers to extend reinforcement learning techniques to solve increasingly complex control tasks. The emerging field of deep reinforcement learning has led to remarkable empirical results in rich and varied domains like robotics, strategy games, and multiagent interaction. This workshop will bring together researchers working at the intersection of deep learning and reinforcement learning, and it will help interested researchers outside of the field gain a high-level view about the current state of the art and potential directions for future contributions.

Sat 8:45 a.m. - 9:00 a.m. [iCal]
Welcome Comments (Talk)
Sat 9:00 a.m. - 9:30 a.m. [iCal]
Grandmaster Level in StarCraft II using Multi-Agent Reinforcement Learning - Invited Talk (Talk)
Oriol Vinyals
Sat 9:30 a.m. - 10:00 a.m. [iCal]
  • "Playing Dota 2 with Large Scale Deep Reinforcement Learning" - OpenAI, Christopher Berner, Greg Brockman, Brooke Chan, Vicki Cheung, Przemyłsaw Dębiak, Christy Dennison, David Farhi, Quirin Fischer, Shariq Hashme, Chris Hesse, Rafal Józefowicz, Scott Gray, Catherine Olsson, Jakub Pachocki, Michael Petrov, Henrique Pondé de Oliveira Pinto, Jonathan Raiman, Tim Salimans, Jeremy Schlatter, Jonas Schneider, Szymon Sidor, Ilya Sutskever, Jie Tang, Filip Wolski, Susan Zhang
  • "Efficient Exploration with Self-Imitation Learning via Trajectory-Conditioned Policy" - Yijie Guo, Jongwook Choi, Marcin Moczulski, Samy Bengio, Mohammad Norouzi, Honglak Lee
  • "Efficient Visual Control by Latent Imagination" - Danijar Hafner, Timothy Lillicrap, Jimmy Ba, Mohammad Norouzi
Jie Tang, Yijie Guo, Danijar Hafner
Sat 10:00 a.m. - 10:30 a.m. [iCal]
Bayes-Adaptive Deep Reinforcement Learning via Meta-Learning - Invited Talk (Talk)
Shimon Whiteson
Sat 10:30 a.m. - 11:00 a.m. [iCal]
Coffee Break (Break)
Sat 11:00 a.m. - 11:30 a.m. [iCal]
Optico: A Framework for Model-Based Optimization with MuJoCo Physics - Invited Talk (Talk)
Emo Todorov
Sat 11:30 a.m. - 12:00 p.m. [iCal]
  • "Adaptive Online Planning for Lifelong Reinforcement Learning" - Kevin Lu, Igor Mordatch, Pieter Abbeel
  • "Interactive Fiction Games: A Colossal Adventure" - Matthew Hausknecht, Prithviraj V Ammanabrolu, Marc-Alexandre Côté, Xingdi Yuan
  • "Hierarchy is Exploration: An Empirical Analysis of the Benefits of Hierarchy" - Ofir Nachum, Haoran Tang, Xingyu Lu, Shixiang Gu, Honglak Lee, Sergey Levine
Kevin Lu, Matthew Hausknecht, Ofir Nachum
Sat 12:00 p.m. - 12:30 p.m. [iCal]
  • Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model - Julian Schrittwieser, Ioannis Antonoglou, Thomas Hubert, Karen Simonyan, Laurent Sifre, Simon Schmitt, Arthur Guez, Edward Lockhart, Demis Hassabis, Thore Graepel, Timothy Lillicrap, David Silver
  • Is a Good Representation Sufficient for Sample Efficient Reinforcement Learning? - Simon S. Du, Sham M. Kakade, Ruosong Wang, Lin F. Yang
  • Solving Rubik's Cube with a Robot Hand - 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
David Silver, Simon Du, Matthias Plappert
Sat 1:30 p.m. - 2:00 p.m. [iCal]

(Talk title and abstract TBD.)

Emma Brunskill
Sat 2:00 p.m. - 2:30 p.m. [iCal]
  • "Striving for Simplicity in Off-Policy Deep Reinforcement Learning" - Rishabh Agarwal, Dale Schuurmans, Mohammad Norouzi
  • "Adversarial Policies: Attacking Deep Reinforcement Learning" - Adam R Gleave, Michael Dennis, Neel Kant, Cody Wild, Sergey Levine, Stuart Russell
  • "A Simple Randomization Technique for Generalization in Deep Reinforcement Learning" - Kimin Lee, Kibok Lee, Jinwoo Shin, Honglak Lee
Rishabh Agarwal, Adam Gleave, Kimin Lee
Sat 2:30 p.m. - 4:00 p.m. [iCal]
Poster Session
Matthia Sabatelli, Adam Stooke, Amir Abdi, Paulo Rauber, Leonard Adolphs, Ian Osband, Hardik Meisheri, Karol Kurach, Johannes Ackermann, Matt Benatan, GUO ZHANG, Chen Tessler, Dinghan Shen, Mikayel Samvelyan, Riashat Islam, Murtaza Dalal, Luke Harries, Andrey Kurenkov, Konrad Żołna, Sudeep Dasari, Kristian Hartikainen, Ofir Nachum, Kimin Lee, Markus Holzleitner, Vu Nguyen, Francis Song, Christopher Grimm, Leno Silva, Yuping Luo, Yifan Wu, Alex Lee, Thomas Paine, Wei-Yang Qu, Daniel Graves, Yannis Flet-Berliac, Yunhao Tang, Suraj Nair, Matthew Hausknecht, Akhil Bagaria, Simon Schmitt, Bowen Baker, Paavo Parmas, Benjamin Eysenbach, Lisa Lee, Siyu Lin, Daniel Seita, Abhishek Gupta, Riley Simmons-Edler, Yijie Guo, Kevin Corder, Vikash Kumar, Scott Fujimoto, Adam Lerer, Ignasi Clavera Gilaberte, Nick Rhinehart, Ashvin Nair, Ge Yang, Lingxiao Wang, Sungryull Sohn, JFernando Hernandez-Garcia, Xian Yeow Lee, Rupesh Srivastava, Khimya Khetarpal, Chenjun Xiao, Luckeciano Carvalho Melo, Rishabh Agarwal, Tianhe (Kevin) Yu, Glen Berseth, Devendra Singh Chaplot, Jie Tang, Anirudh Srinivasan, Tharun Medini, Aaron Havens, Misha Laskin, Asier Mujika, Rohan Saphal, Joe Marino, Alex Ray, Joshua Achiam, Ajay Mandlekar, Zhuang Liu, Danijar Hafner, Zhiwen Tang, Ted Xiao, Michael Walton, Jeff Druce, Ferran Alet, Zhang-Wei Hong, Stephanie Chan, Anusha Nagabandi, Hao Liu, Hao Sun, Ge Liu, Dinesh Jayaraman, JD Co-Reyes, Sophia Sanborn
Sat 4:00 p.m. - 5:00 p.m. [iCal]

16:00 - 16:15 Learn to Move: Walk Around 16:15 - 16:30 Animal Olympics 16:30 - 16:45 Robot open-Ended Autonomous Learning (REAL) 16:45 - 17:00 MineRL

Sat 5:00 p.m. - 5:30 p.m. [iCal]
Assessing the Robustness of Deep RL Algorithms - Invited Talk (Talk)
Michael L. Littman
Sat 5:30 p.m. - 6:00 p.m. [iCal]

(Topic and panelists TBA.)

Author Information

Pieter Abbeel (UC Berkeley & covariant.ai)

Pieter Abbeel is Professor and Director of the Robot Learning Lab at UC Berkeley [2008- ], Co-Director of the Berkeley AI Research (BAIR) Lab, Co-Founder of covariant.ai [2017- ], Co-Founder of Gradescope [2014- ], Advisor to OpenAI, Founding Faculty Partner AI@TheHouse venture fund, Advisor to many AI/Robotics start-ups. He works in machine learning and robotics. In particular his research focuses on making robots learn from people (apprenticeship learning), how to make robots learn through their own trial and error (reinforcement learning), and how to speed up skill acquisition through learning-to-learn (meta-learning). His robots have learned advanced helicopter aerobatics, knot-tying, basic assembly, organizing laundry, locomotion, and vision-based robotic manipulation. He has won numerous awards, including best paper awards at ICML, NIPS and ICRA, early career awards from NSF, Darpa, ONR, AFOSR, Sloan, TR35, IEEE, and the Presidential Early Career Award for Scientists and Engineers (PECASE). Pieter's work is frequently featured in the popular press, including New York Times, BBC, Bloomberg, Wall Street Journal, Wired, Forbes, Tech Review, NPR.

Chelsea Finn (Stanford University)
Joelle Pineau
David Silver (DeepMind)
Satinder Singh (University of Michigan)
Joshua Achiam (UC Berkeley, OpenAI)
Carlos Florensa (UC Berkeley)
Christopher Grimm (University of Michigan)
Haoran Tang (UC Berkeley)
Vivek Veeriah (University of Michigan)

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