  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.
