Multi-Agent Reinforcement Learning

Research project implementing multi-agent reinforcement learning algorithms for coordinated multi-robot exploration tasks in unknown environments.

This project explores the application of reinforcement learning techniques to multi-robot systems, focusing on collaborative exploration strategies. The system enables multiple autonomous agents to work together efficiently, sharing information and coordinating their actions to maximize coverage of unknown environments.

Key Features:

• Multi-agent coordination algorithms
• Reinforcement learning-based exploration strategies
• Information sharing and communication protocols
• Scalable architecture for varying numbers of agents
• Simulation and real-world validation

Technologies: Python, PyTorch, Reinforcement Learning, Multi-Agent Systems, ROS, Gazebo

Developed as part of research work at C-MORE Lab, focusing on improving multi-robot coverage efficiency through advanced machine learning techniques.