DeepMind MAS Environment is a Python library that provides a standardized interface for building and simulating multi-agent reinforcement learning tasks. It allows users to configure number of agents, define observation and action spaces, and customize reward structures. The framework supports agent-to-agent communication channels, performance logging, and rendering capabilities. Researchers can seamlessly integrate DeepMind MAS Environment with popular RL libraries such as TensorFlow and PyTorch to benchmark new algorithms, test communication protocols, and analyze both discrete and continuous control domains.

VacuumWorld is an open-source simulation platform designed to facilitate the development and evaluation of multi-agent reinforcement learning algorithms. It provides grid-based environments where virtual vacuum cleaner agents operate to detect and remove dirt patches across customizable layouts. Users can adjust parameters such as grid size, dirt distribution, stochastic movement noise, and reward structures to model diverse scenarios. The framework includes built-in support for agent communication protocols, real-time visualization dashboards, and logging utilities for performance tracking. With simple Python APIs, researchers can quickly integrate their RL algorithms, compare cooperative or competitive strategies, and conduct reproducible experiments, making VacuumWorld ideal for academic research and teaching.