Playing Atari Games in OCaml

This presentation explores the application of machine learning to Atari games by leveraging OCaml's functional type safety alongside PyTorch and TensorFlow for GPU-accelerated deep learning. It details how supervised convolutional neural networks achieve over 99.5% image classification accuracy and how reinforcement learning agents utilizing Deep Q-Learning can master games like Breakout and Pong, albeit with significantly slower training times than humans. The talk concludes by highlighting the technical interoperability achieved through custom OCaml bindings and outlines future research directions in transfer learning and generalization across varying game environments.