  The growing popularity of generative flow networks (GFlowNets or GFNs) from a
range of researchers with diverse backgrounds and areas of expertise
necessitates a library which facilitates the testing of new features such as
training losses that can be easily compared to standard benchmark
implementations, or on a set of common environments. torchgfn is a PyTorch
library that aims to address this need. It provides users with a simple API for
environments and useful abstractions for samplers and losses. Multiple examples
are provided, replicating and unifying published results. The code is available
in https://github.com/saleml/torchgfn.
