Panel: The future of reinforcement learning
- Geoff Gordon, Emma Brunskill, Craig Boutilier, Sham Kakade, Joelle Pineau, Csaba Szepesvari | Microsoft Research Montreal, Stanford University, Google, Microsoft Research NYC, Facebook/McGill University, DeepMind/University of Alberta
- Microsoft Research Summit 2021 | Reinforcement Learning
This panel brings together a variety of experts from industry and academia to discuss the question, what is the future of reinforcement learning? Reinforcement learning is an important research area in AI currently, and it has been an important research area in human and animal behavior since at least the middle of the 20th century. More recently, reinforcement learning research has been energized by a series of positive results, often based on deep models, in areas such as personalization and game-playing. However, there remain a wide variety of open questions, both theoretical and practical. We’ll gather expert perspectives on which open questions are the most important as well as where the likely answers might come from.
Learn more about the 2021 Microsoft Research Summit: https://Aka.ms/researchsummit (opens in new tab)
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Geoff Gordon
Partner Researcher
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Emma Brunskill
Associate Professor, Computer Science Department
Stanford University
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Craig Boutilier
Principal Scientist
Google
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Sham Kakade
Sr. Principal Researcher
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Csaba Szepesvari
Team Lead / Professor
DeepMind / University of Alberta
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Joelle Pineau
Managing Director / Associate Professor
Facebook AI Research / McGill University
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Reinforcement Learning
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Opening remarks: Reinforcement Learning
- Katja Hofmann
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Research talk: Evaluating human-like navigation in 3D video games
- Raluca Georgescu,
- Ida Momennejad
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Research talk: Maia Chess: A human-like neural network chess engine
- Reid McIlroy-Young
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Fireside chat: Opportunities and challenges in human-oriented AI
- Ashley Llorens,
- Katja Hofmann,
- Siddhartha Sen
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Research talk: Making deep reinforcement learning industrially applicable
- Jiang Bian,
- Tie-Yan Liu
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Panel: Generalization in reinforcement learning
- Mingfei Sun,
- Roberta Raileanu,
- Wendelin Böhmer
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Research talk: Project Dexter: Machine learning and automatic decision-making for robotic manipulation
- Andrey Kolobov,
- Ching-An Cheng
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Research talk: Breaking the deadly triad with a target network
- Shangtong Zhang
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Panel: The future of reinforcement learning
- Geoff Gordon,
- Emma Brunskill,
- Craig Boutilier
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Closing remarks: Reinforcement Learning
- John Langford
