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Deep Coordinator 框架优化机器人求解器超参数

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了 Deep Coordinator，一个新颖的深度展开框架，旨在优化多智能体机器人中分布式求解器的超参数调整。该系统在执行过程中动态调整 ADMM-DDP 求解器的超参数，这是非凸优化器首次实现的功能。在涉及汽车和四旋翼飞行器的模拟中进行测试，Deep Coordinator 实现了与传统方法相当的轨迹质量，速度快了 9.44 倍，并且在比其训练系统大八倍的系统上保持了性能。 AI

影响 该框架可以显著加速复杂的机器人多智能体模拟和实际部署。

排序理由 该集群包含一篇详细介绍机器人优化新框架的研究论文。

在 arXiv cs.LG 阅读 →

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Deep Coordinator 框架优化机器人求解器超参数

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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Hunter Kuperman, Minchan Jung, Rahul V. Ghosh, Alex Oshin, Evangelos A. Theodorou ·

    深度展开的协调

    arXiv:2606.19920v1 Announce Type: cross Abstract: Distributed optimization is a highly scalable and structurally transparent technique to solve multi-agent robotics problems; however, such methods often suffer from the need for highly-specialized, problem-specific hyperparameter …

  2. arXiv cs.MA (Multiagent) TIER_1 English(EN) · Evangelos A. Theodorou ·

    深度展开协调

    Distributed optimization is a highly scalable and structurally transparent technique to solve multi-agent robotics problems; however, such methods often suffer from the need for highly-specialized, problem-specific hyperparameter tunings. In this work, we propose Deep Coordinator…

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