English(EN) Improving Sample Efficiency in Multi-Agent Reinforcement Learning for Simulated Football Games via Exploration

新的MARL方法将足球AI训练效率提高了13.3%

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-05 04:00

研究人员开发了一种方法，以提高模拟足球比赛中AI的多智能体强化学习（MARL）的样本效率。通过引入随机网络蒸馏奖励，新方法鼓励更好的探索，与最先进的TiZero方法相比，在每个训练阶段的样本效率提高了13.3%。这种增强不仅加快了训练速度，还提高了AI球员的泛化能力和适应性，使MARL更适用于实际游戏开发。 AI

影响加速开发更强大、更适应复杂模拟环境（如体育游戏）的AI智能体。

排序理由学术论文，详细介绍了提高多智能体强化学习样本效率的新方法。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.LG TIER_1 English(EN) · Amir Baghi, Jens Sj\"olund, Joakim Bergdahl, Linus Gissl\'en, Alessandro Sestini · 2026-08-05 04:00

Improving Sample Efficiency in Multi-Agent Reinforcement Learning for Simulated Football Games via Exploration

arXiv:2503.13077v2 Announce Type: replace Abstract: Multi-agent reinforcement learning has shown promise in learning cooperative behaviors in team-based environments. However, such methods often demand extensive training time, which inhibits their application for game-AI in stand…

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Improving Sample Efficiency in Multi-Agent Reinforcement Learning for Simulated Football Games via Exploration

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