This developed-and-maintained package is a good approach towards furthering RL development; as the writeups state, the biggest problem in RL is subtle bugs from an implementation which don't cause an error but tank learning performance. (+ loggers/utils to help debug things)
Granted, a lot of RL thought pieces/examples on places like Medium.com take an existing RL implementation without many tweaks, run it on a new task, and see what happens. A better RL library might make this workflow more prevalent; hence why it's very important for researchers to make their pipelines transparent.
In my opinion PyTorch code is easier to understand and debug for newcomers. Code is definitely lacking in documentation, but whenever there was a tradeoff between clarity and modularity in the end I've chosen modularity. Ideally I would like others to be able to take bits and pieces and incorporate into their projects to speed up time to delivery of their ideas.
Granted, a lot of RL thought pieces/examples on places like Medium.com take an existing RL implementation without many tweaks, run it on a new task, and see what happens. A better RL library might make this workflow more prevalent; hence why it's very important for researchers to make their pipelines transparent.