We present a novel paradigm named Herd's Eye View (HEV) that adopts a global perspective derived from multiple agents to boost decision-making capabilities of reinforcement learning (RL) agents in multi-agent environments, specifically in the context of game AI. The HEV approach utilizes cooperative perception to empower RL agents with a global reasoning ability, enhancing their decision-making. We demonstrate the effectiveness of the HEV within simulated game environments and highlight its superior performance compared to traditional ego-centric perception models. This work contributes to cooperative perception and multi-agent reinforcement learning by offering a more realistic and efficient perspective for global coordination and decision-making within game environments. Moreover, our approach promotes broader AI applications beyond gaming by addressing constraints faced by AI outside of this field. The code is available on GitHub.

翻译：我们提出一种名为“群智视角”（Herd's Eye View, HEV）的新范式，通过多智能体协同视角提升强化学习（RL）智能体在多智能体环境中的决策能力，特别聚焦于游戏AI领域。HEV方法利用协同感知赋予RL智能体全局推理能力，显著增强其决策效能。我们在模拟游戏环境中验证了HEV的有效性，并突出其相对于传统自我中心感知模型的性能优势。本工作通过为游戏环境中的全局协调与决策提供更真实高效的分析视角，推动了协同感知与多智能体强化学习发展。此外，我们的方法通过解决AI在游戏领域外的应用约束，促进了更广泛的人工智能应用。相关代码已开源发布于GitHub。