Scaling Game Simulations with DataFlow – Towards Data Science

Dataflow is a great tool for building out scalable data pipelines, but it can also be useful in different domains, such as scientific computing. One of the ways that I've been using Google's Cloud Dataflow tool recently is for simulating gameplay of different automated players. Years ago I built an automated Tetris player as part of the AI course at Cal Poly. I used a metaheuristic search approach, which required significant training time to learn the best values for the hyperparameters. I was able to code a distributed version of the system to scale up the approach, but it took significant effort to deploy on the 20 machines in the lab.