LEARNING
超越GPU主导范式的机器人强化学习异构架构
Yufei Jia, Zhanxiang Cao, Mingrui Yu, Heng Zhang, Shenyu Chen, Dixuan Jiang, Meng Li, Xiaofan Li, Yiyang Liu, Junzhe Wu, Zheng Li, XiLin Fang, Tingyu Cui, Shengcheng Fu, Haoyang Li, Anqi Wang, Zifan Wang, Dongjie Zhu, Chenyu Cao, Zhenbiao Huang
- 发表年份
- 2026
- 访问权限
- 开放获取
摘要
本文提出了一种名为UniLab的异构CPU仿真/GPU学习架构，通过统一运行时解耦CPU并行仿真与GPU策略更新，显著提升了端到端训练效率。实验表明，在相同硬件配置下，UniLab可将代表性机器人控制任务的训练效率提升3-10倍。
关键词
heterogeneous architecturereinforcement learningGPU simulationCPU simulationtraining efficiency
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