Multi Agent Deep Reinforcement Learning

Multi-agent deep reinforcement learning (MADRL) focuses on training multiple AI agents to collaborate or compete within a shared environment, aiming to optimize collective performance through learning. Current research emphasizes developing efficient algorithms like MADDPG and variations of Q-learning, often incorporating transformer networks for improved feature representation and handling complex interactions, and exploring different training paradigms such as centralized training with decentralized execution. This field is significant for its potential to solve complex real-world problems across diverse domains, including autonomous driving, robotics, network optimization, and resource management, by enabling more robust and adaptable systems.