We present the alpha version of TorchRL, the Reinforcement-Learning dedicated PyTorch domain library.

TorchRL is an open-source Reinforcement Learning (RL) library for PyTorch.

It provides pytorch and python-first, low and high level abstractions for RL that are intended to be efficient, documented and properly tested. The code is aimed at supporting research in RL. Most of it is written in python in a highly modular way, such that researchers can easily swap components, transform them or write new ones with little effort.

TorchRL provide low-level primitives to efficiently collect data across a wide range of libraries and efficiently train algorithms on these data. We provide data-carrying structures that make it easy to write efficient codes in parallel and distributed settings.

We will present a few examples of basic usage of the library and some results on specific tasks (robotics, games and others).