Off-Line, Off-Policy RL for Real-World Decision Making at Facebook - #448
Published:Jan 18, 2021 23:16
•1 min read
•Practical AI
Analysis
This article summarizes a podcast episode from Practical AI featuring Jason Gauci, a Software Engineering Manager at Facebook AI. The discussion centers around Facebook's Reinforcement Learning platform, Re-Agent (Horizon). The conversation covers the application of decision-making and game theory within the platform, including its use in ranking, recommendations, and e-commerce. The episode also delves into the distinctions between online/offline and on/off policy model training, placing Re-Agent within this framework. Finally, the discussion touches upon counterfactual causality and safety measures in model results. The article provides a high-level overview of the topics discussed in the podcast.
Key Takeaways
- •The podcast discusses Facebook's Re-Agent (Horizon) platform for Reinforcement Learning.
- •The platform is used for decision-making in areas like ranking, recommendations, and e-commerce.
- •The episode covers the differences between online/offline and on/off policy model training, and counterfactual causality.
Reference
“The episode explores their Reinforcement Learning platform, Re-Agent (Horizon).”