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Training AI with strong and rich strategies in multi-agent environmentsremains an important research topic in Deep Reinforcement Learning (DRL). TheAI's strength is closely related to its diversity of strategies, and thisrelationship can guide us to train AI with both strong and rich strategies. Toprove this point, we propose Diversity is Strength (DIS), a novel DRL trainingframework that can simultaneously train multiple kinds of AIs. These AIs arelinked through an interconnected history model pool structure, which enhancestheir capabilities and strategy diversities. We also design a model evaluationand screening scheme to select the best models to enrich the model pool andobtain the final AI. The proposed training method provides diverse,generalizable, and strong AI strategies without using human data. We tested ourmethod in an AI competition based on Google Research Football (GRF) and won the5v5 and 11v11 tracks. The method enables a GRF AI to have a high level on both5v5 and 11v11 tracks for the first time, which are under complex multi-agentenvironments. The behavior analysis shows that the trained AI has richstrategies, and the ablation experiments proved that the designed modulesbenefit the training process.

Diversity is Strength: Mastering Football Full Game with Interactive Reinforcement Learning of Multiple AIs