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Apr, 2024
多智能体强化学习的可证明高效信息导向采样算法
Provably Efficient Information-Directed Sampling Algorithms for Multi-Agent Reinforcement Learning
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Qiaosheng Zhang, Chenjia Bai, Shuyue Hu, Zhen Wang, Xuelong Li
TL;DR
该研究设计和分析了一组基于信息导向采样(IDS)原则的新型多智能体强化学习(MARL)算法,这些算法受到信息论基础概念的启发,在两人零和马尔可夫博弈和多人一般和博弈等MARL环境中被证明具有高样本效率。
Abstract
This work designs and analyzes a novel set of algorithms for
multi-agent reinforcement learning
(MARL) based on the principle of
information-directed sampling
(IDS). These algorithms draw inspiration from foundat
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