PyTorch has ascended as a premier machine learning framework, yet it lacks a native and comprehensive library for decision and control tasks suitable for large development teams dealing with complex real-world data and environments. To address this issue, we propose TorchRL, a generalistic control library for PyTorch that provides well-integrated, yet standalone components. We introduce a new and flexible PyTorch primitive, the TensorDict, which facilitates streamlined algorithm development across the many branches of Reinforcement Learning (RL) and control. We provide a detailed description of the building blocks and an extensive overview of the library across domains and tasks. Finally, we experimentally demonstrate its reliability and flexibility and show comparative benchmarks to demonstrate its computational efficiency. TorchRL fosters long-term support and is publicly available on GitHub for greater reproducibility and collaboration within the research community. The code is open-sourced on GitHub.

翻译：PyTorch已成为领先的机器学习框架，但目前缺乏适用于处理复杂真实世界数据与环境的决策及控制任务的原生综合库。为解决此问题，我们提出TorchRL——一个面向PyTorch的通用控制库，其提供高度集成且独立的组件。我们引入了一种新颖灵活的PyTorch原语TensorDict，它可简化强化学习与控制多分支领域的算法开发流程。我们详细描述了该库的构建模块，并全面概述了其在跨领域任务中的应用。最后，通过实验验证其可靠性与灵活性，并通过对比基准测试证明其计算效率。TorchRL致力于长期维护，已在GitHub上开源，以促进研究社区的复现与协作。代码已在GitHub上开源发布。