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Transformer Guided Coevolution: Improved Team Selection in Multiagent Adversarial Team Games

arXiv 2410.13769 ↗First posted 17 Oct 2024cs.AI cs.MA cs.NEcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 17 of its 45 references that are in the map. First posted on arXiv 17 Oct 2024.

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17 of 45 references.

References · 17

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5 more
  1. 2019Microsoft Translator at WMT 2019: Towards Large-Scale Document-Level Neural Machine Translationcited by 56
  2. 2023TiZero: Mastering Multi-Agent Football with Curriculum Learning and Self-Playcited by 20
  3. 2021A coevolutionary approach to deep multi-agent reinforcement learningcited by 3
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  5. 2024Evaluating Collaborative Autonomy in Opposed Environments using Maritime Capture-the-Flag Competitionscited by 1

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