A Survey of Domain-Specific Architectures for Reinforcement Learning

A Survey of Domain-Specific Architectures for Reinforcement Learning

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Reinforcement learning algorithms have been very successful at solving sequential decision-making quillingcard.com discount code problems in many different problem domains.However, their training is often time-consuming, with training times ranging from multiple hours to weeks.The development of domain-specific architectures for reinforcement learning promises faster computation times, decreased experiment turn-around time, and improved energy efficiency.This paper presents a review of hardware architectures for the acceleration of reinforcement advantage household spot & crevice spray learning algorithms.FPGA-based implementations are the focus of this work, but GPU-based approaches are considered as well.

Both tabular and deep reinforcement learning algorithms are included in this survey.The techniques employed in different implementations are highlighted and compared.Finally, possible areas for future work are suggested, based on the preceding discussion of existing architectures.