Transformer Guided Coevolution: Improved Team Formation in Multiagent Adversarial Games

• URL: http://arxiv.org/abs/2410.13769v2
• Date: Thu, 31 Oct 2024 23:59:53 GMT
• Title: Transformer Guided Coevolution: Improved Team Formation in Multiagent Adversarial Games
• Authors: Pranav Rajbhandari, Prithviraj Dasgupta, Donald Sofge,
• Abstract summary: We propose an 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 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.
• Score: 1.2338485391170533
• License:
• Abstract: 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.

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