Abstract
Multi-agent systems (MAS) involve multiple autonomous agents interacting within an environment, making them ideal for applications in robotics, simulations, and complex decision-making. Neural networks have shown great promise in enhancing the functionality and decision-making capabilities of multi-agent systems by enabling agents to learn from their interactions and environments. This article explores the design and application of neural networks in multi-agent systems, including the challenges of training neural networks in decentralized environments, cooperation and competition among agents, and the use of deep reinforcement learning (DRL) to optimize agent behaviors. It also discusses future trends and directions in the field of MAS and neural network integration
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