Today we’re joined by Jason Gauci, a Software Engineering Manager at Facebook AI.
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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.
Connect with Jason!
- 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
- Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems
- Causality 101 with Robert Osazuwa Ness
- Check out our TWIML Presents: series page!
- Register for the TWIML Newsletter
- Check out the official TWIMLcon:AI Platform video packages here!
- Download our latest eBook, The Definitive Guide to AI Platforms!
“More On That Later” by Lee Rosevere licensed under CC By 4.0