Learning to Play Tic-Tac-Toe with Jax (joe-antognini.github.io)

A new article has emerged detailing the process of training a neural network to play Tic-Tac-Toe using reinforcement learning with the Jax framework. The tutorial emphasizes pedagogical clarity over code optimization, allowing developers to implement the model on standard laptops with training times around 15 seconds. It introduces the PGX library for representing game states and managing game logic, simplifying the training process by enabling parallel game execution. This development is significant for the AI/ML community as it showcases practical reinforcement learning concepts in a comprehensible manner, making it accessible for those looking to understand deep Q Networks (DQNs). The article outlines the workflow, from initializing game states to implementing a fully connected neural network architecture that predicts optimal moves based on the board's current state. As a result, users can evaluate their model's performance against random players and gradually enhance their understanding of reinforcement learning by applying temporal difference learning to improve decision-making in gameplay.