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Horizon: Deep Reinforcement Learning at Scale

Horizon: Deep Reinforcement Learning at Scale

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“To build a decision-making system, we must provide answers to two sets of questions: (1) “”What will happen if I make decision X?”” and (2) “”How should I pick which decision to make?””. Typically, the first set of questions are answered with supervised learning: we build models to forecast whether someone will click on an ad, or visit a post. The second set of questions are more open-ended. In this talk, we will dive into how we can answer “”how”” questions, starting with heuristics and search. This will lead us to bandits, reinforcement learning, and Horizon: an open-source platform for training and deploying reinforcement learning models at massive scale. At Facebook, we are using Horizon, built using PyTorch 1.0 and Apache Spark, in a variety of AI-related and control tasks, spanning recommender systems, marketing & promotion distribution, and bandwidth optimization. The talk will cover the key components of Horizon and the lessons we learned along the way that influenced the development of the platform.”
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