Enabling High Data Throughput Reinforcement Learning on GPUs: A Domain Agnostic Framework for Data-Driven Scientific Research
<|reference_start|>Enabling High Data Throughput Reinforcement Learning on GPUs: A Domain Agnostic Framework for Data-Driven Scientific Research: We introduce WarpSci, a domain agnostic framework designed to overcome crucial system bottlenecks encountered in the application of reinforcement learning to intricate environments with vast datasets featuring high-dimensional observation or action spaces. Notably, our framework eliminates the need for data transfer between the CPU and GPU, enabling the concurrent execution of thousands of simulations on a single or multiple GPUs. This high data throughput architecture proves particularly advantageous for data-driven scientific research, where intricate environment models are commonly essential.<|reference_end|>
@article{lan2024enabling,
    title={Enabling High Data Throughput Reinforcement Learning on GPUs: A Domain
  Agnostic Framework for Data-Driven Scientific Research},
    author={Tian Lan, Huan Wang, Caiming Xiong, Silvio Savarese},
    journal={arXiv preprint arXiv:2408.00930},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.00930},
    primaryClass={cs.LG cs.AI}
}