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A Survey of Domain-Specific Architectures for Reinforcement Learning
2022
IEEE Access
Reinforcement learning algorithms have been very successful at solving sequential decision-making problems in many different problem domains. However, their training is often timeconsuming, with training times ranging from multiple hours to weeks. The development of domain-specific architectures for reinforcement learning promises faster computation times, decreased experiment turnaround time, and improved energy efficiency. This paper presents a review of hardware architectures for the
doi:10.1109/access.2022.3146518
fatcat:ufrhsktrkza2jjjoi6kdm23rgi