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Off-Line, Off-Policy RL for Real-World Decision Making at Facebook - #448
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Sam Charrington

· 1h 02m

PodcastThe TWIML AI Podcast

Off-Line, Off-Policy RL for Real-World Decision Making at Facebook - #448

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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.

The complete show notes for this episode can be found at twimlai.com/go/448.

Original source

This story was published by The TWIML AI Podcast and written by Sam Charrington. SyncAI.news shows a preview; the complete article is on the publisher's site.

Read the full story on twimlai.com

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