Opening remarks: Reinforcement Learning
- Katja Hofmann | Microsoft Research Cambridge
- Microsoft Research Summit 2021 | Reinforcement Learning
Reward-based learning has been a foundational component in human psychology. With reinforcement learning, researchers are using reward systems to accelerate AI, where techniques for gaming, robotics, and autonomous systems are being created with an emphasis on real-world impact in the future. In this track, you’ll learn about how researchers are using AI to power innovation in artificial environments, like simulators or games, and are thinking about bridging the gap to real-world applications for industry and other areas of impact.
Learn more about the 2021 Microsoft Research Summit: https://Aka.ms/researchsummit (opens in new tab)
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
