TiZero：通过课程学习和自我游戏掌握多智能体足球
TiZero: Mastering Multi-Agent Football with Curriculum Learning and Self-Play
摘要：
多智能体足球是人工智能研究中一个尚未解决的挑战。现有的工作集中在解决游戏的简化场景，或者利用专家演示。在本文中，我们开发了一个多智能体系统来玩完整的11对11游戏模式，没有演示。这种游戏模式包含了对现代强化学习算法的主要挑战;多智能体协调，长期规划和非传递性。为了应对这些挑战，我们提出了TiZero;一个自我进化的多智能体系统，从头开始学习。TiZero引入了几项创新，包括自适应课程学习，一种新颖的自我游戏策略，以及一个共同优化多个代理策略的目标。在实验中，它在Google Research Football环境中的表现远远优于以前的系统，将胜率提高了30%以上。为了展示TiZero的创新的普遍性，他们在足球之外的几个环境中进行了评估;过度烹饪，多智能体粒子环境，井字游戏和连接四。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
