Keynote: Key research challenges for real world reinforcement learning
- John Langford | Microsoft Research NYC
- Microsoft Research Summit 2021 | Reinforcement Learning
Reinforcement learning has begun to have a broad impact on society with deployments and systems that make a real and tangible difference in how the world works. This is just scratching the surface though—in the future, reinforcement learning will become a ubiquitous method to improve systems. How do we get there? What challenges do we need to overcome on the way? What is the order we should expect these things to arrive in? How do we crack these things?
Learn more about the 2021 Microsoft Research Summit: https://Aka.ms/researchsummit (opens in new tab)
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John Langford
Partner Researcher Manager
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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
