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We present a novel perception model named Herd's Eye View (HEV) that adopts aglobal perspective derived from multiple agents to boost the decision-makingcapabilities of reinforcement learning (RL) agents in multi-agent environments,specifically in the context of game AI. The HEV approach utilizes cooperativeperception to empower RL agents with a global reasoning ability, enhancingtheir decision-making. We demonstrate the effectiveness of the HEV withinsimulated game environments and highlight its superior performance compared totraditional ego-centric perception models. This work contributes to cooperativeperception and multi-agent reinforcement learning by offering a more realisticand efficient perspective for global coordination and decision-making withingame environments. Moreover, our approach promotes broader AI applicationsbeyond gaming by addressing constraints faced by AI in other fields such asrobotics. The code is available at https://github.com/andrewnash/Herds-Eye-View

Herd's Eye View: Improving Game AI Agent Learning with Collaborative Perception