王若男, 董琦. 基于学习机制的多智能体博弈决策方法[J]. 工程科学学报. DOI: 10.13374/j.issn2095-9389.2023.08.08.003
引用本文: 王若男, 董琦. 基于学习机制的多智能体博弈决策方法[J]. 工程科学学报. DOI: 10.13374/j.issn2095-9389.2023.08.08.003
Multi-Agent Game Decision-Making Method Based on Learning Mechanism[J]. Chinese Journal of Engineering. DOI: 10.13374/j.issn2095-9389.2023.08.08.003
Citation: Multi-Agent Game Decision-Making Method Based on Learning Mechanism[J]. Chinese Journal of Engineering. DOI: 10.13374/j.issn2095-9389.2023.08.08.003

基于学习机制的多智能体博弈决策方法

Multi-Agent Game Decision-Making Method Based on Learning Mechanism

  • 摘要: 强化学习是人工智能领域的一个重要分支,以其显著的优越性成为了多智能体博弈决策的主流方法。然而,多智能体强化学习仍存在着维度爆炸、训练样本少、难以迁移等固有挑战。因此,为了提升多智能体强化学习算法的性能,本文从基于学习机制的角度出发,探索学习机制与强化学习的深度融合。本文综述了基于学习机制的多智能体强化学习方法,对比了各类方法的优势和局限性,分析了在博弈决策、博弈策略学习框架、可解释的多智能体学习方法等领域中基于学习机制的多智能体强化学习方法的发展前景。

     

    Abstract: Reinforcement learning is an important branch in the field of artificial intelligence, and has become a mainstream method in the field of multi-agent game decision-making due to its remarkable advantages. However, reinforcement learning still has inherent challenges such as dimension explosion, few training samples, and difficulty in transfer. Therefore, in order to improve the performance of multi-agent reinforcement learning methods, this paper explores and studies the deep integration of learning mechanism and reinforcement learning from the perspective of learning mechanism. This paper summarizes the multi-agent reinforcement learning methods based on learning mechanisms, compares the advantages and limitations of various methods, and analyzes the learning mechanism-based Application status and development prospects of multi-agent reinforcement learning methods.

     

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