LEARNING
ACPO：面向多智能体强化学习的智能体链式策略优化
Daiki E. Matsunaga, Junho Na, Tri Wahyu Guntara, Scott Sanner, Pascal Poupart, Jongmin Lee, Kee-Eung Kim
- 发表年份
- 2026
- 访问权限
- 开放获取
摘要
本文提出了一种新的多智能体强化学习方法ACPO，通过将联合策略梯度分解为每个智能体独立的项，实现了在集中训练分散执行范式下的有效优化。该方法利用串行化决策视角，让智能体依次基于对先前动作的信念提交动作，从而在保持独立训练的同时实现协调。
关键词
multi-agent reinforcement learningpolicy gradientcentralized training decentralized executioncoordinationjoint policy optimization
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