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.

Aeiva is a developer-first platform that enables you to create, deploy, and evaluate autonomous AI agents within flexible simulation environments. It features a plugin-based engine for environment definition, intuitive APIs to customize agent decision loops, and built-in metrics collection for performance analysis. The framework supports integration with OpenAI Gym, PyTorch, and TensorFlow, plus real-time web UI for monitoring live simulations. Aeiva’s benchmarking tools let you organize agent tournaments, record results, and visualize agent behaviors to fine-tune strategies and accelerate multi-agent AI research.