  The promotion of large-scale applications of reinforcement learning (RL)
requires efficient training computation. While existing parallel RL frameworks
encompass a variety of RL algorithms and parallelization techniques, the
excessively burdensome communication frameworks hinder the attainment of the
hardware's limit for final throughput and training effects on a single desktop.
In this paper, we propose Spreeze, a lightweight parallel framework for RL that
efficiently utilizes a single desktop hardware resource to approach the
throughput limit. We asynchronously parallelize the experience sampling,
network update, performance evaluation, and visualization operations, and
employ multiple efficient data transmission techniques to transfer various
types of data between processes. The framework can automatically adjust the
parallelization hyperparameters based on the computing ability of the hardware
device in order to perform efficient large-batch updates. Based on the
characteristics of the "Actor-Critic" RL algorithm, our framework uses dual
GPUs to independently update the network of actors and critics in order to
further improve throughput. Simulation results show that our framework can
achieve up to 15,000Hz experience sampling and 370,000Hz network update frame
rate using only a personal desktop computer, which is an order of magnitude
higher than other mainstream parallel RL frameworks, resulting in a 73%
reduction of training time. Our work on fully utilizing the hardware resources
of a single desktop computer is fundamental to enabling efficient large-scale
distributed RL training.

Machine Learning;Artificial Intelligence;Distributed, Parallel, and Cluster Computing