TiZero: Mastering Multi-Agent Football with Curriculum Learning and Self-Play
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.
Keywords
Cite
@article{arxiv.2302.07515,
title = {TiZero: Mastering Multi-Agent Football with Curriculum Learning and Self-Play},
author = {Fanqi Lin and Shiyu Huang and Tim Pearce and Wenze Chen and Wei-Wei Tu},
journal= {arXiv preprint arXiv:2302.07515},
year = {2023}
}
Comments
The 22nd International Conference on Autonomous Agents and Multiagent Systems(AAMAS2023)