HFO_DQN combines Python and TensorFlow to deliver a complete pipeline for training soccer agents using Deep Q-Networks. Users can clone the repository, install dependencies including the HFO simulator and Python libraries, and configure training parameters in YAML files. The framework implements experience replay, target network updates, epsilon-greedy exploration, and reward shaping tailored for the half field offense domain. It features scripts for agent training, performance logging, evaluation matches, and plotting results. Modular code structure allows integration of custom neural network architectures, alternative RL algorithms, and multi-agent coordination strategies. Outputs include trained models, performance metrics, and behavior visualizations, facilitating research in reinforcement learning and multi-agent systems.

Shumai is a powerful tensor library designed for JavaScript and TypeScript, created by Facebook Research (FAIR). The library stands out for its high performance, network connectivity, and differentiable capabilities. Built using Bun and Flashlight, it enables developers to seamlessly integrate deep learning and machine learning functionalities into web applications. It supports features such as GPU computation, making it ideal for complex scientific computations and model training. Shumai is aimed at providing a robust environment for developing advanced machine learning models in a TypeScript ecosystem.