Horizon
An end-to-end platform for applied reinforcement learning and contextual bandits, built with PyTorch for production decision-making systems.
What is Horizon?
ReAgent is an end-to-end platform for applied reinforcement learning and contextual bandits, originally developed at Facebook. It provides tools for training, evaluating, and serving decision-making models in production environments where simulators aren't available. The platform supports batch offline training and counterfactual policy evaluation to safely test new policies without deployment.
Machine learning engineers and researchers building production reinforcement learning systems for recommendation engines, optimization tasks, and decision-making applications. Particularly valuable for teams working with batch data in real-world environments.
Developers choose ReAgent for its comprehensive production-ready workflow, support for both classic RL algorithms and specialized recommender system methods, and robust counterfactual evaluation tools. Its focus on batch offline learning makes it uniquely suited for real-world applications where live experimentation is risky.
Overview
A platform for Reasoning systems (Reinforcement Learning, Contextual Bandits, etc.)
Use Cases
Best For
- Building production reinforcement learning systems for recommendation engines
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