MagicBlocks transforms the way users create and experience virtual worlds with its powerful AI-driven tools. This AI agent simplifies designing 3D environments by automating intricate tasks, making it accessible for both beginners and experienced creators. Users can easily manipulate elements, customize environments, and visualize their ideas in real-time, ensuring a seamless creative workflow from concept to execution.

Patrolling-Zoo offers a flexible framework enabling users to create and experiment with multi-agent patrolling tasks in Python. The library includes a variety of grid-based and graph-based environments, each simulating surveillance, monitoring, and coverage scenarios. Users can configure the number of agents, map size, topology, reward functions, and observation spaces. Through compatibility with PettingZoo and Gym APIs, it supports seamless integration with popular reinforcement learning algorithms. This environment facilitates benchmarking and comparing MARL techniques under consistent settings. By providing standard scenarios and tools to customize new ones, Patrolling-Zoo accelerates research in autonomous robotics, security surveillance, search-and-rescue operations, and efficient area coverage using multi-agent coordination strategies.

Scalable MADDPG is a research-oriented framework for multi-agent reinforcement learning, offering a scalable implementation of the MADDPG algorithm. It features centralized critics during training and independent actors at runtime for stability and efficiency. The library includes Python scripts to define custom environments, configure network architectures, and adjust hyperparameters. Users can train multiple agents in parallel, monitor metrics, and visualize learning curves. It integrates with OpenAI Gym-like environments and supports GPU acceleration via TensorFlow. By providing modular components, Scalable MADDPG enables flexible experimentation on cooperative, competitive, or mixed multi-agent tasks, facilitating rapid prototyping and benchmarking.

AutoDRIVE Cooperative MARL is a GitHub-hosted framework combining the AutoDRIVE urban driving simulator with adaptable multi-agent reinforcement learning algorithms. It includes training scripts, environment wrappers, evaluation metrics, and visualization tools to develop and benchmark cooperative driving policies. Users can configure agent observation spaces, reward functions, and training hyperparameters. The repository supports modular extensions, enabling custom task definitions, curriculum learning, and performance tracking for autonomous vehicle coordination research.