Unitlab is a dynamic platform designed to streamline the data annotation process. Leveraging AI, it offers a collaborative environment where users can efficiently manage, label, and enhance data. The platform supports on-premises solutions and integrated labeling services, making it suitable for diverse industries requiring high-quality data labeling. By automating labor-intensive tasks, Unitlab significantly accelerates data annotation, ensuring high accuracy and scalability. It’s an ideal choice for AI teams, startups, and companies developing extensive training data and AI-driven products.

Mean-Field MARL provides a robust Python framework for implementing and evaluating mean-field multi-agent reinforcement learning algorithms. It approximates large-scale agent interactions by modeling the average effect of neighboring agents via mean-field Q-learning. The library includes environment wrappers, agent policy modules, training loops, and evaluation metrics, enabling scalable training across hundreds of agents. Built on PyTorch for GPU acceleration, it supports customizable environments like Particle World and Gridworld. Modular design allows easy extension with new algorithms, while built-in logging and Matplotlib-based visualization tools track rewards, loss curves, and mean-field distributions. Example scripts and documentation guide users through setup, experiment configuration, and result analysis, making it ideal for both research and prototyping of large-scale multi-agent systems.