Multi Agent Learning

Multi-agent learning (MAL) focuses on developing algorithms enabling multiple autonomous agents to learn and interact effectively within a shared environment, often to achieve a common goal or compete for resources. Current research emphasizes improving coordination and cooperation strategies, particularly in decentralized settings, exploring diverse model architectures like neural networks and reinforcement learning algorithms such as Q-learning and Proximal Policy Optimization, often incorporating game-theoretic concepts. The field's significance lies in its potential to create more robust, efficient, and adaptable systems across various domains, from robotics and autonomous driving to resource management and economic modeling.