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 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 Pulling Breast Collars 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 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 Zeolite Powder 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.