Workshop
Fri Jun 14th 08:30 AM -- 12:30 PM @ Seaside Ballroom
Reinforcement Learning for Real Life
Yuxi Li · Alborz Geramifard · Lihong Li · Csaba Szepesvari · Tao Wang





Reinforcement learning (RL) is a general learning, predicting, and decision making paradigm. RL provides solution methods for sequential decision making problems as well as those can be transformed into sequential ones. RL connects deeply with optimization, statistics, game theory, causal inference, sequential experimentation, etc., overlaps largely with approximate dynamic programming and optimal control, and applies broadly in science, engineering and arts.

RL has been making steady progress in academia recently, e.g., Atari games, AlphaGo, visuomotor policies for robots. RL has also been applied to real world scenarios like recommender systems and neural architecture search. See a recent collection about RL applications at https://medium.com/@yuxili/rl-applications-73ef685c07eb. It is desirable to have RL systems that work in the real world with real benefits. However, there are many issues for RL though, e.g. generalization, sample efficiency, and exploration vs. exploitation dilemma. Consequently, RL is far from being widely deployed. Common, critical and pressing questions for the RL community are then: Will RL have wide deployments? What are the issues? How to solve them?

The goal of this workshop is to bring together researchers and practitioners from industry and academia interested in addressing practical and/or theoretical issues in applying RL to real life scenarios, review state of the arts, clarify impactful research problems, brainstorm open challenges, share first-hand lessons and experiences from real life deployments, summarize what has worked and what has not, collect tips for people from industry looking to apply RL and RL experts interested in applying their methods to real domains, identify potential opportunities, generate new ideas for future lines of research and development, and promote awareness and collaboration. This is not "yet another RL workshop": it is about how to successfully apply RL to real life applications. This is a less addressed issue in the RL/ML/AI community, and calls for immediate attention for sustainable prosperity of RL research and development.

08:30 AM optional early-bird posters (Poster Session)
08:50 AM opening remarks by organizers (Opening Remarks)
09:00 AM invited talk by David Silver (Deepmind): AlphaStar: Mastering the Game of StarCraft II (Talk)
David Silver
09:20 AM invited talk by John Langford (Microsoft Research): How do we make Real World Reinforcement Learning revolution? (Talk)
John Langford
09:40 AM invited talk by Craig Boutilier (Google Research): Reinforcement Learning in Recommender Systems: Some Challenges (Talk)
Craig Boutilier
10:00 AM posters (Poster Session)
Zhengxing Chen, Juan Jose Garau Luis, Ignacio Albert Smet, Aditya Modi, Sabina Tomkins, Riley Simmons-Edler, Hongzi Mao, Alex Irpan, Hao Lu, Rose Wang, Subhojyoti Mukherjee, Aniruddh Raghu, Syed Arbab Mohd Shihab, Byung Hoon Ahn, Rasool Fakoor, Pratik Chaudhari, Elena Smirnova, Min-hwan Oh, Xiaocheng Tang, Tony Qin, Qingyang Li, Marc Brittain, Ian Fox, Supratik Paul, Xiaofeng Gao, Yinlam Chow, Gabriel Dulac-Arnold, Ofir Nachum, Nikos Karampatziakis, Bharath Balaji, Supratik Paul, Ali Davody, Djallel Bouneffouf, Himanshu Sahni, Soo Kim, Andrey Kolobov, Alexander Amini, Yao Liu, Xinshi Chen, , Craig Boutilier
10:30 AM coffee break (Coffee Break)
11:00 AM panel discussion with Craig Boutilier (Google Research), Emma Brunskill (Stanford), Chelsea Finn (Google Brain, Stanford, UC Berkeley), Mohammad Ghavamzadeh (Facebook AI), John Langford (Microsoft Research) and David Silver (Deepmind) (Panel Discussion)
Peter Stone, Craig Boutilier, Emma Brunskill, Chelsea Finn, John Langford, David Silver, Mohammad Ghavamzadeh
12:00 PM optional posters (Poster Session)