Horizon: Facebook's Open Source Applied Reinforcement Learning Platform

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. Deep reinforcement learning (RL) is poised to revolutionize how autonomous systems are built. In recent years, it has been shown to achieve state-of-theart performance on a wide variety of complicated tasks (Mnih et al., 2015; Lillicrap et al., 2015; Schulman et al., 2015; Van Hasselt et al., 2016; Schulman et al., 2017), where being successful requires learning complex relationships between high dimensional state spaces, actions, and long term rewards. However, the current implementations of the latest advances in this field have mainly been tailored to academia, focusing on fast prototyping and evaluating performance on simulated benchmark environments.