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WarpDrive: Extremely Fast End-to-End Deep Multi-Agent Reinforcement Learning on a GPU
Deep reinforcement learning (RL) is a powerful framework to train decision-making models in complex dynamical environments. However, RL can be slow as it learns through repeated interaction with a simulation of the environment.
We present WarpDrive, a flexible, lightweight, and easy-to-use open-source RL framework that implements end-toend multi-agent RL on a single GPU (Graphics Processing Unit), building on PyCUDA and PyTorch. Using the extreme parallelization capability of GPUs, WarpDrive enables ordersof-magnitude faster RL compared to common implementations that blend CPU simulations and GPU models.
2021: Tian Lan, Sunil Srinivasa, Stephan Zheng
https://arxiv.org/pdf/2108.13976v1.pdf
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By Rob
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Deep reinforcement learning (RL) is a powerful framework to train decision-making models in complex dynamical environments. However, RL can be slow as it learns through repeated interaction with a simulation of the environment.
We present WarpDrive, a flexible, lightweight, and easy-to-use open-source RL framework that implements end-toend multi-agent RL on a single GPU (Graphics Processing Unit), building on PyCUDA and PyTorch. Using the extreme parallelization capability of GPUs, WarpDrive enables ordersof-magnitude faster RL compared to common implementations that blend CPU simulations and GPU models.
2021: Tian Lan, Sunil Srinivasa, Stephan Zheng
https://arxiv.org/pdf/2108.13976v1.pdf