TorchRL: The PyTorch Reinforcement Learning Domain Library
We present TorchRL, the new reinforcement learning library from the PyTorch ecosystem team. 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. Given reinforcement learning is a nascent space and the largest production applications are likely to come from tomorrow’s research, we deliberately focus on supporting emerging areas. Through simple examples, we illustrate the capabilities of this library and its ease of use in various RL subfields.
Vincent Moens is a research engineer at Meta working on developing the TorchRL library — the Reinforcement Learning ecosystem library for PyTorch. TorchRL will be an open-source library built on top of PyTorch to support research in RL through a set of low and high-level reusable primitives that are common across RL frameworks. In 2013 Vincent graduated from Med School in Brussels and during the course of his residency in neurology he undertook a PhD in cognitive and computational neuroscience at UCLouvain, Belgium.
After completing his PhD, he worked in the financial sector as a Machine Learning Scientist for a couple of years in London. He then switched back to research and worked as a Senior Machine Learning Research Scientist at Huawei, where he integrated the reinforcement learning team and provided expertise in generative modeling for model-based reinforcement learning.