Cooperative Search Environment provides a flexible, gym-compatible multi-agent reinforcement learning environment tailored for cooperative search tasks in both discrete grid and continuous spaces. Agents operate under partial observability and can share information based on customizable communication topologies. The framework supports predefined scenarios like search-and-rescue, dynamic target tracking, and collaborative mapping, with APIs to define custom environments and reward structures. It integrates seamlessly with popular RL libraries such as Stable Baselines3 and Ray RLlib, includes logging utilities for performance analysis, and offers built-in visualization tools for real-time monitoring. Researchers can adjust grid sizes, agent counts, sensor ranges, and reward sharing mechanisms to evaluate coordination strategies and benchmark new algorithms effectively.

MultiAgent-Systems-StarCraft2-PySC2-Raw offers a complete toolkit for developing, training, and evaluating multiple AI agents in StarCraft II. It exposes low-level controls for unit movement, targeting, and abilities, while allowing flexible reward design and scenario configuration. Users can easily plug in custom neural network architectures, define team-based coordination strategies, and record metrics. Built on top of PySC2, it supports parallel training, checkpointing, and visualization, making it ideal for advancing research in cooperative and adversarial multi-agent reinforcement learning.

multiagent_envs delivers a modular set of Python-based environments tailored for multi-agent reinforcement learning research and development. It includes scenarios like cooperative navigation, predator-prey, social dilemmas, and competitive arenas. Each environment lets you define the number of agents, observation features, reward functions, and collision dynamics. The framework integrates seamlessly with popular RL libraries such as Stable Baselines and RLlib, allowing vectorized training loops, parallel execution, and easy logging. Users can extend existing scenarios or create new ones by following a simple API, accelerating experimentation with algorithms like MADDPG, QMIX, and PPO in a consistent, reproducible setup.