English(EN) Sparse Sensor Placement in Multi-Agent Reinforcement Learning Control of Rayleigh-Bénard Convection

新的MARL框架实现了流体控制的稀疏传感器放置

作者 PulseAugur 编辑部 · [1 个来源] · 2026-06-29 12:49

研究人员开发了一个新颖的框架，用于瑞利-贝纳尔对流多智能体强化学习（MARL）控制中的稀疏传感器放置。该方法使用有序非凸分组正则化和迭代重加权分组正则化，从密集专家策略中提炼出稀疏策略。实验表明，与Proximal Policy Optimization基线相比，多智能体Transformer策略提供了更稳定的训练，并且稀疏学徒策略保持了与密集专家相当的控制性能。所提出的方法实现了显著的稀疏性，将每个智能体的观测大小从360减少到12，同时保留了整体训练趋势，并为传感器高效控制提供了一种实用的方法。 AI

影响这项研究通过减少数据吞吐量，为复杂环境中的传感器高效控制系统提供了一条途径。

排序理由学术论文，详细介绍了多智能体强化学习的新方法。[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) · Sebastian Peitz · 2026-06-29 12:49

瑞利-贝纳德对流多智能体强化学习控制中的稀疏传感器放置

This paper studies sparse sensor placement for control of Rayleigh-Bénard convection with multi-agent reinforcement learning. We train dense expert policies with windowed observations and distill sparse apprentice policies by supervised learning with grouped regularization on enc…

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瑞利-贝纳德对流多智能体强化学习控制中的稀疏传感器放置

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