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.

翻译：强化学习（RL）大规模应用的推广需要高效的训练计算能力。现有并行强化学习框架虽涵盖了多种RL算法与并行化技术，但其冗繁的通信框架导致单台桌面计算机难以达到硬件极限吞吐量与训练效果。本文提出Spreeze——一种轻量级并行RL框架，能高效利用单台桌面计算机硬件资源逼近吞吐量极限。我们采用异步并行化经验采样、网络更新、性能评估与可视化操作，并运用多种高效数据传输技术实现进程间多类数据传递。该框架可根据硬件设备的计算能力自动调整并行化超参数，从而执行高效的大批量更新。基于"Actor-Critic"强化学习算法的特性，本框架采用双GPU分别独立更新Actor与Critic网络，进一步提升了吞吐量。仿真结果表明，在仅使用个人桌面计算机的情况下，本框架可实现高达15,000Hz的经验采样速率与370,000Hz的网络更新帧率，较其他主流并行强化学习框架提升一个数量级，并减少73%的训练时间。本研究关于充分利用单台桌面计算机硬件资源的成果，为实现高效大规模分布式强化学习训练奠定了基础。