PEARL: Enabling Portable, Productive, and High-Performance Deep Reinforcement Learning using Heterogeneous Platforms-Reference-Cited by-同舟云学术

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PEARL: Enabling Portable, Productive, and High-Performance Deep Reinforcement Learning using Heterogeneous Platforms

Published:2024-05-07 Issue: Volume:22 Page:41-50
ISSN:
Container-title:Proceedings of the 21st ACM International Conference on Computing Frontiers
language:
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Author:

Meng Yuan¹^ORCID,Kinsner Michael²^ORCID,Singh Deshanand²^ORCID,Iyer Mahesh²^ORCID,Prasanna Viktor¹^ORCID

Affiliation:

1. University of Southern California, United States

2. Intel Corporation, United States

Funder

NSF (National Science Foundation)

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3649153.3649193

Reference28 articles.

1. 2021. Intel Heterogeneous DevCloud. https://devcloud.intel.com/oneapi/

2. 2022. Intel Extension for PyTorch. https://github.com/intel/intel-extension-for-pytorch

3. AMD. 2022. AMD Heterogeneous Accelerated Compute Clusters. https://www.amd-haccs.io/

4. Scientific Computing With Python on High-Performance Heterogeneous Systems

5. Greg Brockman Vicki Cheung Ludwig Pettersson Jonas Schneider John Schulman Jie Tang and Wojciech Zaremba. 2016. OpenAI Gym. arXiv:arXiv:1606.01540

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