Publication

Horizon: Facebook’s Open Source Applied Reinforcement Learning Platform

ArXiv


Abstract

In this paper we present Horizon, Facebook’s open source applied reinforcement learning (RL) platform. Horizon is an end-to-end platform designed to solve industry applied RL problems where datasets are large (millions to billions of observations), the feedback loop is slow (vs. a simulator), and experiments must be done with care because they don’t run in a simulator. Unlike other RL platforms, which are often designed for fast prototyping and experimentation, Horizon is designed with production use cases as top of mind. The platform contains workflows to train popular deep RL algorithms and includes data preprocessing, feature transformation, distributed training, counterfactual policy evaluation, and optimized serving. We also showcase real examples of where models trained with Horizon significantly outperformed and replaced supervised learning systems at Facebook.

Related Publications

All Publications

Towards Automated Neural Interaction Discovery for Click-Through Rate Prediction

Qingquan Song, Dehua Cheng, Eric Zhou, Jiyan Yang, Yuandong Tian, Xia Hu

KDD - August 1, 2020

Vid2Game: Controllable Characters Extracted from Real-World Videos

Oran Gafni, Lior Wolf, Yaniv Taigman

ICLR - March 10, 2020

Word-level Speech Recognition with a Letter to Word Encoder

Ronan Collobert, Awni Hannun, Gabriel Synnaeve

ICML - July 13, 2020

Compositionality and Generalization in Emergent Languages

Rahma Chaabouni, Eugene Kharitonov, Diane Bouchacourt, Emmanuel Dupoux, Marco Baroni

ACL - July 4, 2020

To help personalize content, tailor and measure ads, and provide a safer experience, we use cookies. By clicking or navigating the site, you agree to allow our collection of information on and off Facebook through cookies. Learn more, including about available controls: Cookies Policy