问手臂结构的dma_map_single内件EN做这个实践的主要目的就是让我们活学活用, 从0开始搭建一个强化学习框架。之前我们在强化学习系列教程中学习到了很多强化学习的知识, 了解了各种算法应该怎样运用, 从最简单的 Q-Learning到结合神经网络的 DQN, 再到做连续动作的 DDPG 以及分布式训练的 A3C 和 DPPO。但是我们却没有
* support iommu at the driver level, so it also matches to the DMA address. * Hence, this helper currently just performs the cache operation, then returns * straight-mapped dma_address, which is intended to be set to the register of * the DMA device. * * @vaddr: address of the buff...
要么一步到位直接把cache关了，要么只在dma传输数据过程中才关cache。
* support iommu at the driver level, so it also matches to the DMA address. * Hence, this helper currently just performs the cache operation, then returns * straight-mapped dma_address, which is intended to be set to the register of * the DMA device. * * @vaddr: address of the buff...
其中，DeviceC和DeviceD之间链路Cost是100，其他链路的Cost均为10。当前从DeviceA到DeviceF的最优路径是...