Keynote: Key research challenges for real world 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)
- 轨迹:
- Reinforcement Learning
- 日期：
- 演讲者：
- John Langford
- 所属机构：
- Microsoft Research NYC
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John Langford
Partner Researcher Manager
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Reinforcement Learning
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Research talk: Reinforcement learning with preference feedback
Speakers:- Aadirupa Saha
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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:- Kiante 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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