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.

我们提出了一种轻量级并行RL框架Spreeze，它能有效利用单个桌面硬件资源接近吞吐极限，通过异步并行化经验采样、网络更新、性能评估和可视化操作，并采用多种高效数据传输技术在进程之间传输各种类型的数据，基于计算能力自动调整并行超参数以进行高效的大批量更新，进一步改进吞吐量，模拟结果表明我们的框架能以一个人的个人桌面计算机每秒15,000次经验采样和370,000次网络更新的帧速率实现，比其他主流并行RL框架高一个数量级，训练时间减少了73%，充分利用单个桌面计算机的硬件资源是实现高效大规模分布式RL训练的基础。

Spreeze: 高吞吐并行强化学习框架