Deploying reinforcement learning in production using Ray and Amazon SageMaker

Reinforcement learning (RL) is used to automate decision-making in a variety of domains, including games, autoscaling, finance, robotics, recommendations, and supply chain. Launched at AWS re:Invent 2018, Amazon SageMaker RL helps you quickly build, train, and deploy policies learned by RL. Ray is an open-source distributed execution framework that makes it easy to scale your Python applications. Amazon SageMaker RL uses the RLlib library that builds on the Ray framework to train RL policies. This post walks you through the tools available in Ray and Amazon SageMaker RL that help you address challenges such as scale, security, iterative development, and operational cost when you use RL in production.