Off-Line, Off-Policy RL for Real-World Decision Making at Facebook with Jason Gauci
EPISODE 448
|
JANUARY
18,
2021
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About this Episode
Today we're joined by Jason Gauci, a Software Engineering Manager at Facebook AI.
In our conversation with Jason, we explore their Reinforcement Learning platform, Re-Agent (Horizon). We discuss the role of decision making and game theory in the platform and the types of decisions they're using Re-Agent to make, from ranking and recommendations to their eCommerce marketplace.
Jason also walks us through the differences between online/offline and on/off policy model training, and where Re-Agent sits in this spectrum. Finally, we discuss the concept of counterfactual causality, and how they ensure safety in the results of their models.
About the Guest
Jason Gauci
Facebook AI
Resources
- Horizon (Re-Agent) Blog Post
- Paper: Horizon: Facebook's Open Source Applied Reinforcement Learning Platform
- Facebook's FBLearner Platform with Aditya Kalro
- Spiral: Self-tuning services via real-time machine learning
- Self-Tuning Services via Real-Time Machine Learning with Vladimir Bychkovsky
- Meet Michelangelo: Uber's Machine Learning Platform
- Scaling Machine Learning at Uber with Mike Del Balso
- AnyScale
- Grid.ai
- Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems
- Causality 101 with Robert Osazuwa Ness
