Multi-Agent Deep Reinforcement Learning: Cooperative Strategies for Complex Environments

Abstract

One effective method for handling complicated issues with many agents interacting in dynamic settings is Multi-Agent Deep Reinforcement Learning, or MADRL. cooperation tactics in MADRL, when agents cooperate to accomplish common objectives in intricate, multi-dimensional settings. We take a look at a number of learning methods, such as shared policies, communication protocols that allow agents to work together effectively, and centralized training with decentralized execution. We test these tactics in simulated settings for improving group performance on tasks like multi-agent navigation and robotic swarm management. Our results show that MADRL in a cooperative setting improves decision-making for individual agents and produces better, more scalable solutions to real-world problems. We also go over some of the most important problems with multi-agent systems, including scalability, non-stationarity, and coordination, and we suggest some directions for future work in MADRL.