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.

AutoDRIVE Cooperative MARL is an open-source framework designed to train and deploy cooperative multi-agent reinforcement learning (MARL) policies for autonomous driving tasks. It integrates with realistic simulators to model traffic scenarios like intersections, highway platooning, and merging. The framework implements centralized training with decentralized execution, enabling vehicles to learn shared policies that maximize overall traffic efficiency and safety. Users can configure environment parameters, choose from baseline MARL algorithms, visualize training progress, and benchmark agent coordination performance.