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.

翻译：本文研究多智能体对抗团队博弈中的团队选择问题。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已训练种群中选择最优玩家团队。我们将此方法与协同进化深度强化学习相结合，后者训练多样化的个体玩家以供选择。我们在多智能体对抗游戏"海军夺旗"中测试算法，发现BERTeam能够学习到非平凡的团队组合，在面对未知对手时表现优异。在该游戏中，BERTeam的表现优于同样优化团队选择的MCAA算法。