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Deep Reinforcement Learning
Pieter Abbeel · David Silver · Satinder Singh · Joelle Pineau · Joshua Achiam · Rein Houthooft · Aravind Srinivas

Fri Dec 07 05:00 AM -- 03:30 PM (PST) @ Room 220 E
Event URL: https://sites.google.com/view/deep-rl-workshop-nips-2018/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.

Fri 6:00 a.m. - 6:30 a.m.
Talk by Yann Lecunn (Talk)
Fri 6:30 a.m. - 7:00 a.m.


Fri 7:00 a.m. - 7:30 a.m.
Talk by Jacob Andreas (Talk)
Fri 7:30 a.m. - 8:00 a.m.
Coffee (Break)
Fri 8:00 a.m. - 8:30 a.m.
Talk by Sham Kakade (Talk)
Fri 9:00 a.m. - 9:30 a.m.
Talk by Doina Precup (Talk)
Fri 9:30 a.m. - 10:30 a.m.
Lunch (Break)
Fri 10:30 a.m. - 11:00 a.m.
Talk by Satinder Singh (Talk)
Fri 11:00 a.m. - 11:30 a.m.
Contributed Talks (Talks)
Fri 11:30 a.m. - 12:00 p.m.
Talk by Martha White (Talk)
Fri 12:00 p.m. - 1:00 p.m.
Poster Session 1 + Coffee (Poster session)
Tom Van de Wiele, Rui Zhao, JFernando Hernandez-Garcia, Fabio Pardo, Xian Yeow Lee, Xiaolin Andy Li, Marcin Andrychowicz, Jie Tang, Suraj Nair, Juhyeon Lee, Cédric Colas, Ali Eslami, Yen-Chen Wu, Stephen McAleer, Ryan Julian, Yang Xue, Matthia Sabatelli, Pranav Shyam, Alexandros Kalousis, Giovanni Montana, Emanuele Pesce, Felix Leibfried, Zhanpeng He, Chunxiao Liu, Yanjun Li, Yoshihide Sawada, Alexander Pashevich, Tejas Kulkarni, Keiran Paster, Luca Rigazio, Quan Vuong, Hyunggon Park, Minhae Kwon, Rivindu Weerasekera, Shamane Siriwardhanaa, Rui Wang, Ozsel Kilinc, Keith Ross, Yizhou Wang, Simon Schmitt, Thomas Anthony, Evan Cater, Forest Agostinelli, Tegg Sung, Shirou Maruyama, Alex Shmakov, Devin Schwab, Mohammad Firouzi, Glen Berseth, Denis Osipychev, Jesse Farebrother, Jianlan Luo, William Agnew, Peter Vrancx, Jonathan Heek, Catalin Ionescu Ionescu, Haiyan Yin, Megumi Miyashita, Nathan Jay, Noga H. Rotman, Sam Leroux Leroux, Shaileshh Bojja Venkatakrishnan, Henri Schmidt, Jack Terwilliger, Ishan Durugkar, Jonathan Sauder, David Kas, Arash Tavakoli, Alain-Sam Cohen, Philip Bontrager, Adam Lerer, Thomas Paine, Ahmed Khalifa, Rubén Rodriguez, Avi Singh, Yiming Zhang
Fri 1:00 p.m. - 1:30 p.m.
Talk by Jeff Clune (Talk)
Fri 1:30 p.m. - 2:15 p.m.
Contributed Talks (Talks)
Fri 2:15 p.m. - 3:15 p.m.
Poster Session 2 (Poster session)

Author Information

Pieter Abbeel (UC Berkeley | Gradescope | Covariant)

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.

David Silver (DeepMind)
Satinder Singh (University of Michigan)
Joelle Pineau (McGill University)

Joelle Pineau is an Associate Professor and William Dawson Scholar at McGill University where she co-directs the Reasoning and Learning Lab. She also leads the Facebook AI Research lab in Montreal, Canada. She holds a BASc in Engineering from the University of Waterloo, and an MSc and PhD in Robotics from Carnegie Mellon University. Dr. Pineau's research focuses on developing new models and algorithms for planning and learning in complex partially-observable domains. She also works on applying these algorithms to complex problems in robotics, health care, games and conversational agents. She serves on the editorial board of the Journal of Artificial Intelligence Research and the Journal of Machine Learning Research and is currently President of the International Machine Learning Society. She is a recipient of NSERC's E.W.R. Steacie Memorial Fellowship (2018), a Fellow of the Association for the Advancement of Artificial Intelligence (AAAI), a Senior Fellow of the Canadian Institute for Advanced Research (CIFAR) and in 2016 was named a member of the College of New Scholars, Artists and Scientists by the Royal Society of Canada.

Joshua Achiam (UC Berkeley, OpenAI)
Rein Houthooft (Happy Elements)
Aravind Srinivas (UC Berkeley)

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