Ray – Fast and Simple Distributed Computing

We chose Ray because we needed to train many reinforcement learning agents simultaneously. It was important to us to deliver results quickly to people using Pathmind, which simulation modelers use to apply reinforcement learning to industrial operations and supply chains. We use Ray, RLlib and Ray Serve so that businesses can quickly train RL on the cloud and feed RL-based decisions to their operations for use cases that range from optimal warehouse picking to routing autonomous guided vehicles.