AdvCommNet: Enhancing Multi-Agent Interaction in Deep Reinforcement Learning Systems

This paper addresses the challenge of efficient information processing in multi-agent systems by introducing an innovative approach, AdvCommNet. Building on the foundation of deep reinforcement learning, AdvCommNet incorporates advanced kernel functions such as softmax and sigmoid to optimize information interaction and highlight the significance of crucial data. The effectiveness of AdvCommNet is demonstrated through extensive experiments in various simulated multi-agent environments. Our results show a significant improvement over the traditional CommNet algorithm in terms of information processing efficiency and collaborative decision-making capabilities. This research not only addresses limitations in complex interactive scenarios found in existing methodologies but also opens new avenues for future advancements in multi-agent systems.