We present a novel perception model named Herd's Eye View (HEV) that adopts a global perspective derived from multiple agents to boost the 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 in other fields such as robotics. The code is available at https://github.com/andrewnash/Herds-Eye-View

翻译：我们提出了一种名为群体视角（Herd's Eye View, HEV）的新型感知模型，该模型采用源自多个智能体的全局视角，以增强多智能体环境中强化学习（RL）智能体的决策能力，特别是在游戏AI场景中。HEV方法利用协作感知赋予RL智能体全局推理能力，从而提升其决策性能。我们在模拟游戏环境中验证了HEV的有效性，并突显了其相较于传统自我中心感知模型的优越性能。本研究通过为游戏环境中的全局协调与决策提供更真实高效的视角，对协作感知与多智能体强化学习领域作出了贡献。此外，我们的方法通过应对机器人与其他领域AI所面临的约束，推动了AI在游戏之外的更广泛应用。相关代码已开源：https://github.com/andrewnash/Herds-Eye-View