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WarpDrive Sampler for PyCUDA

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Tutorial covering the basics of WarpDrive Sampler using PyCUDA
Latest Version
April 4, 2023
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This is the second tutorial on WarpDrive, a PyCUDA-based framework for extremely parallelized multi-agent reinforcement learning (RL) on a single graphics processing unit (GPU). At this stage, we assume you have read our first tutorial on WarpDrive basics.

In this tutorial, we describe CUDASampler, a lightweight and fast action sampler based on the policy distribution across several RL agents and environment replicas. CUDASampler utilizes the GPU to parallelize operations to efficiently sample a large number of actions in parallel.


  • It reads the distribution on the GPU through Pytorch and samples actions exclusively at the GPU. There is no data transfer.
  • It maximizes parallelism down to the individual thread level, i.e., each agent at each environment has its own random seed and independent random sampling process.
  • It runs much faster than most GPU samplers. For example, it is significantly faster than Pytorch.