用Transformer模型优化多人对抗赛中队伍组合，提升对未知对手的胜率。
Transformer Guided Coevolution: Improved Team Selection in Multiagent Adversarial Team Games
- 基于掩码语言模型训练的Transformer选择最佳玩家组合。
- 在海军夺旗游戏中击败同类算法MCAA，表现更优。
- 适合研究多智能体协同与进化强化学习的读者。
我们研究多人对抗团队游戏中的队伍选择问题。提出BERTeam算法，利用基于Transformer的深度神经网络与掩码语言模型训练，从已训练的玩家群体中选出最优队伍。该方法结合共进化深度强化学习，训练出多样化的个体玩家供选择。在多人对抗游戏《海军夺旗》中测试，发现BERTeam能学习到对抗未见对手时表现良好的非平凡队伍组合。相比同样优化队伍选择的MCAA算法，BERTeam表现更优。
原文摘要 · Abstract (English)
We consider the problem of team selection within multiagent adversarial team games. We propose BERTeam, a novel algorithm that uses a transformer-based deep neural network with Masked Language Model training to select the best team of players from a trained population. We integrate this with coevolutionary deep reinforcement learning, which trains a diverse set of individual players to choose from. We test our algorithm in the multiagent adversarial game Marine Capture-The-Flag, and find that BERTeam learns non-trivial team compositions that perform well against unseen opponents. For this game, we find that BERTeam outperforms MCAA, an algorithm that similarly optimizes team selection.
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