TiZero: Mastering Multi-Agent Football with Curriculum Learning and Self-Play

• URL: http://arxiv.org/abs/2302.07515v1
• Date: Wed, 15 Feb 2023 08:19:18 GMT
• Title: TiZero: Mastering Multi-Agent Football with Curriculum Learning and Self-Play
• Authors: Fanqi Lin, Shiyu Huang, Tim Pearce, Wenze Chen, Wei-Wei Tu
• Abstract summary: TiZero is a self-evolving, multi-agent system that learns from scratch. It outperforms previous systems by a large margin on the Google Research Football environment.
• Score: 19.98100026335148
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multi-agent football poses an unsolved challenge in AI research. Existing work has focused on tackling simplified scenarios of the game, or else leveraging expert demonstrations. In this paper, we develop a multi-agent system to play the full 11 vs. 11 game mode, without demonstrations. This game mode contains aspects that present major challenges to modern reinforcement learning algorithms; multi-agent coordination, long-term planning, and non-transitivity. To address these challenges, we present TiZero; a self-evolving, multi-agent system that learns from scratch. TiZero introduces several innovations, including adaptive curriculum learning, a novel self-play strategy, and an objective that optimizes the policies of multiple agents jointly. Experimentally, it outperforms previous systems by a large margin on the Google Research Football environment, increasing win rates by over 30%. To demonstrate the generality of TiZero's innovations, they are assessed on several environments beyond football; Overcooked, Multi-agent Particle-Environment, Tic-Tac-Toe and Connect-Four.

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