Spinning Up in Deep RL

Re:Spinning Up in Deep RL by minimaxir on Thursday November 08, 2018 @01:37PM (#18408652)

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.

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Re:Spinning Up in Deep RL by jrx on Thursday November 08, 2018 @02:14PM (#18409007)

I've made some effort to provide a set of similar high-quality implementations available in PyTorch: https://blog.millionintegrals.com/vel-pytorch-meets-baseline...

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.

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Re:Spinning Up in Deep RL by mike_mg on Thursday November 08, 2018 @04:37PM (#18410278)

+1 on that, that's a great project.

PyTorch with its explicit state that can be easily examined by hand in PyCharm debugger will be way easier for people coming into the field.

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