  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
