We consider the problem of team formation within multiagent adversarial 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 teams from. We test our algorithm in the multiagent adversarial game Marine Capture-The-Flag, and we 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 formation.

翻译：本文研究多智能体对抗游戏中的团队组建问题。我们提出BERTeam算法，该算法采用基于Transformer的深度神经网络，通过掩码语言模型训练从训练好的智能体群体中筛选最优玩家团队。我们将此方法与协同进化深度强化学习相结合，后者负责训练一组多样化的个体智能体作为团队候选池。我们在多智能体对抗游戏"海军夺旗"中测试该算法，发现BERTeam能够学习到具有非平凡结构的团队配置，这些团队在面对未知对手时表现优异。实验结果表明，在该游戏中BERTeam的性能优于同样优化团队组建的MCAA算法。