English(EN) Hybrid Belief Reinforcement Learning for Efficient Coordinated Spatial Exploration

混合RL框架增强了自主代理的协同空间探索能力

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-21 04:00

研究人员开发了一个混合信念强化学习（HBRL）框架，以改进自主代理的协同空间探索。该框架结合了用于空间信念构建的Log-Gaussian Cox Process（LGCP）和用于信息驱动轨迹的Pathwise Mutual Information规划器。然后，系统将控制权转移给Soft Actor-Critic（SAC）代理，该代理以LGCP阶段的空间不确定性和演示轨迹进行初始化。这种方法旨在提高多UAV无线服务供应和城市环境中按需移动传感等任务的效率并减少冗余。 AI

影响该框架可以提高复杂、现实环境中多代理系统的效率和协调性。

排序理由该集群包含一篇详细介绍强化学习新算法框架的研究论文。

在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Danish Rizvi, David Boyle · 2026-10-21 04:00

用于高效协同空间探索的混合信念强化学习

arXiv:2603.03595v2 Announce Type: replace Abstract: Coordinating multiple autonomous agents to explore and serve spatially heterogeneous demand requires jointly learning unknown spatial patterns and planning trajectories that maximize task performance. Pure model-based approaches…

本文如何被排名

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用于高效协同空间探索的混合信念强化学习

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