A Multi-FPGA Scalable Framework for Deep Reinforcement Learning Through Neuroevolution-Reference-Cited by-同舟云学术

A Multi-FPGA Scalable Framework for Deep Reinforcement Learning Through Neuroevolution

Published:2022 Issue: Volume: Page:47-61
ISSN:0302-9743
Container-title:Applied Reconfigurable Computing. Architectures, Tools, and Applications
language:
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Author:

Laserna Javier,Otero Andrés^ORCID,Torre Eduardo de la^ORCID

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-19983-7_4

Reference19 articles.

1. Asseman, A., Antoine, N., Ozcan, A.S.: Accelerating deep neuroevolution on distributed fpgas for reinforcement learning problems. J. Emerg. Technol. Comput. Syst. 17(2) (2021). https://doi.org/10.1145/3425500

2. Brockman, G., et al.: Openai gym (2016)

3. Chen, T., et al.: Tvm: end-to-end optimization stack for deep learning. arXiv preprint arXiv:1802.04799, vol. 11, no. 20 (2018)

4. Digilent: Pynq-z1 board reference manual (2017). https://reference.digilentinc.com/_media/reference/programmable-logic/pynq-z1/pynq-rm.pdf. Accessed 8 July 2021

5. García, A., Zamacola, R., Otero, A., de la Torre, E.: A dynamically reconfigurable bbnn architecture for scalable neuroevolution in hardware. Electronics 9(5) (2020). https://doi.org/10.3390/electronics9050803, https://www.mdpi.com/2079-9292/9/5/803

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