Opening remarks: 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)
- Track:
- Reinforcement Learning
- Date:
- Speakers:
- Katja Hofmann
- Affiliation:
- Microsoft Research Cambridge
Reinforcement Learning
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Opening remarks: Reinforcement Learning
Speakers:- Katja Hofmann
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Research talk: Reinforcement learning with preference feedback
Speakers:- Aadirupa Saha
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Research talk: Maia Chess: A human-like neural network chess engine
Speakers:- Reid McIlroy-Young
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Research talk: Making deep reinforcement learning industrially applicable
Speakers:- Jiang Bian,
- Tie-Yan Liu
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Panel: Generalization in reinforcement learning
Speakers:- Mingfei Sun,
- Roberta Raileanu,
- Harm van Seijen
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Research talk: Successor feature sets: Generalizing successor representations across policies
Speakers:- Kianté Brantley
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Research talk: Towards efficient generalization in continual RL using episodic memory
Speakers:- Mandana Samiei
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Research talk: Breaking the deadly triad with a target network
Speakers:- Shangtong Zhang
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Panel: The future of reinforcement learning
Speakers:- Geoff Gordon,
- Emma Brunskill,
- Craig Boutilier
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