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Best of luck to you in monetizing your efforts, hope this good publicity will help your cause. Thank you very much for opensourcing reference implementations of
by kirillseva 9y ago
Best of luck to you in monetizing your efforts, hope this good publicity will help your cause. Thank you very much for opensourcing reference implementations of state-of-the art reinforcement learning algorithms.
One thing that would make playing with this tech more interesting to me and other newcomers is a guide on how to create a new environment for gym or universe, sort of a crash course on what steps need to be made in order to apply your algorithms to my existing problems
- k_f 9y agoThanks for your kind words and thanks for your suggestion. I agree it's sensible to provide information on how to connect your problem space to our library. We have some more blogposts on the roadmap and might add that one as well (we had some information on that in the documentation, but it's outdated as of now). Until then I would suggest you take a look at the source of our OpenAI gym connector: https://github.com/reinforceio/tensorforce/blob/master/tensorforce/contrib/openai_gym.py https://github.com/reinforceio/tensorforce/blob/master/tenso... and the environment interface: https://github.com/reinforceio/tensorforce/blob/master/tensorforce/environments/environment.py https://github.com/reinforceio/tensorforce/blob/master/tenso...