MARO (Multi-Agent Resource Optimization) is a Python-based framework designed to support the development and evaluation of multi-agent reinforcement learning agents in supply chain, logistics, and resource management scenarios. It includes environment templates for inventory management, truck scheduling, cross-docking, container rental, and more. MARO offers a unified agent API, built-in trackers for experiment logging, parallel simulation capabilities for large-scale training, and visualization tools for performance analysis. The platform is modular, extensible and integrates with popular RL libraries, enabling reproducible research and rapid prototyping of AI-driven optimization solutions.

The Multi-Agent-based Search and Rescue System in ROS is a robotics framework that leverages ROS for deploying multiple autonomous agents to perform coordinated search and rescue operations. Each agent uses onboard sensors and ROS topics for real-time mapping, obstacle avoidance, and target detection. A central coordinator assigns tasks dynamically based on agent status and environment feedback. The system can be run in Gazebo or on actual robots, enabling researchers and developers to test and refine multi-robot cooperation, communication protocols, and adaptive mission planning under realistic conditions.

This open-source project delivers a complete multi-agent reinforcement learning framework built on PyTorch and Unity ML-Agents. It offers decentralized DDPG algorithms, environment wrappers, and training scripts. Users can configure agent policies, critic networks, replay buffers, and parallel training workers. Logging hooks allow TensorBoard monitoring, while modular code supports custom reward functions and environment parameters. The repository includes sample Unity scenes demonstrating collaborative navigation tasks, making it ideal for extending and benchmarking multi-agent scenarios in simulation.

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.

Multiagent_system offers a comprehensive toolkit for constructing and managing multi-agent environments. Users can define custom simulation scenarios, specify agent behaviors, and leverage pre-implemented algorithms such as DQN, PPO, and MADDPG. The framework supports synchronous and asynchronous training, enabling agents to interact concurrently or in turn-based setups. Built-in communication modules facilitate message passing between agents for cooperative strategies. Experiment configuration is streamlined via YAML files, and results are logged automatically to CSV or TensorBoard. Visualization scripts help interpret agent trajectories, reward evolution, and communication patterns. Designed for research and production workflows, Multiagent_system seamlessly scales from single-machine prototypes to distributed training on GPU clusters.

SeeAct is designed to empower vision-language agents with a two-stage pipeline: a planning module powered by large language models generates subgoals based on observed scenes, and an execution module translates subgoals into environment-specific actions. A perception backbone extracts object and scene features from images or simulations. The modular architecture allows easy replacement of planners or perception networks and supports evaluation on AI2-THOR, Habitat, and custom environments. SeeAct accelerates research on interactive embodied AI by providing end-to-end task decomposition, grounding, and execution.

This repository aggregates a comprehensive collection of multi-agent system components and distributed AI coordination techniques. It provides implementations of consensus algorithms, contract net negotiation protocols, auction-based task allocation, coalition formation strategies, and inter-agent communication frameworks. Users can leverage built-in simulation environments to model and test agent behaviors under varied network topologies, latency scenarios, and failure modes. The modular design allows developers and researchers to integrate, extend, or customize individual coordination modules for applications in robotics swarms, IoT device collaboration, smart grids, and distributed decision-making systems.

Applied Intuition specializes in providing software solutions tailored for the autonomous vehicle industry. Their platform allows developers to create realistic simulations, enabling extensive testing and validation of AI driving systems in a range of virtual environments. This ensures safety and efficiency in real-world applications. The tools also integrate seamlessly with existing workflows, making it easier for teams to transition from development to deployment.

multi_agent_celar is designed as a modular AI platform enabling emergent-language communication among multiple intelligent agents in simulated environments. Users can define agent behaviors via policy files, configure environment parameters, and launch coordinated training sessions where agents evolve their own communication protocols to solve cooperative tasks. The framework includes evaluation scripts, visualization tools, and support for scalable experiments, making it ideal for research on multi-agent collaboration, emergent language, and decision-making processes.

Open ACN is a robust AI platforms and frameworks solution designed for building decentralized multi-agent systems. It offers a suite of consensus protocols tailored for agent cooperation, ensuring reliable decision-making across geodistributed nodes. The framework includes modular communication layers, customizable strategy plug-ins, and a built-in simulation environment for end-to-end testing. Developers can define agent behaviors, deploy across Linux, macOS, Windows, or Docker, and leverage real-time logging and monitoring tools. By providing extensible APIs and seamless integration with existing machine learning models, Open ACN simplifies complex orchestration tasks, fostering interoperable, resilient autonomous networks suitable for applications in robotics, supply chain automation, decentralized finance, and IoT.

BotPlayers is a versatile open-source framework designed to streamline the development and deployment of AI-driven game-playing agents. It features a flexible environment abstraction layer that supports screen scraping, web APIs, or custom simulation interfaces, allowing bots to interact with various games. The framework includes built-in reinforcement learning algorithms, genetic algorithms, and rule-based heuristics, along with tools for data logging, model checkpointing, and performance visualization. Its modular plugin system enables developers to customize sensors, actions, and AI policies in Python or Java. BotPlayers also offers YAML-based configuration for rapid prototyping and automated pipelines for training and evaluation. With cross-platform support on Windows, Linux, and macOS, this framework accelerates experimentation and production of intelligent game agents.