English(EN) Finite-Sample Convergence in Networked Average Reward MARL: Decentralization Pitfalls and Entropy Remedies

MARL研究强调去中心化的陷阱与熵的解决方案

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-02 23:34

一篇新的研究论文探讨了网络化多智能体强化学习（MARL）系统中去中心化学习的挑战。该研究提出了不可能结果，表明由于多智能体和部分可观察性，实现全局最优策略的收敛是困难的。然而，该论文还提出了一种具有有限样本收敛保证的去中心化Actor-Critic算法，可收敛到近似平稳点。此外，它证明了引入固定状态熵正则化可以改善目标景观并产生更强的全局收敛保证。 AI

影响这项研究解决了多智能体协调中的基本挑战，可能影响未来的自主系统和分布式AI。

排序理由关于特定机器学习技术的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.MA (Multiagent) 阅读 →

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arXiv cs.MA (Multiagent) TIER_1 English(EN) · Adam Vierman · 2026-11-02 23:34

有限样本下网络化平均奖励MARL的收敛性：去中心化的陷阱与熵的补救

We study the problem of average reward Multi-Agent Reinforcement Learning (MARL) where agents interact within a network. Each agent in the network conducts local updates with information within its $k$-hop neighborhood and collaboratively maximizes the overall reward of the entir…

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有限样本下网络化平均奖励MARL的收敛性：去中心化的陷阱与熵的补救

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