tensorforce

I have been wolfing down RL articles, videos and publications after a intro to deep learning via Manning's Deep Learning for some time now and while the overall concept of RL is easy to grasp (agents, actions and state etc) some of the finer details and processes are quite confusing. I am tempted to blame inconsistency across terminology and implementations for this lack of understanding but I suspect it has more to do with approaching this field through the lens of a developer and not a researcher or academic. Trying to understand the code without grasping the "science" of the mechanisms completely. Either way if you feel to be in a similar spot check out this resource: https://reinforce.io and their respective Github repo: https://github.com/reinforceio/tensorforce. Just reading through their code, and documentation has made a lot of

Thanks 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blogposts on the roadmap and might add that one as well (we had some information on that in the documentation, but it's outdated as of now).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/tenso... and the environment interface: https://github.com/reinforceio/tensorforce/blob/master/tenso...

Working on a deep reinforcement learning library that can be used in practical applications and not just simulations. The idea is that there might be many developers/ml enthusiasts interested in deep reinforcement learning, but existing research code is often tightly coupled with simulations like OpenAI Gym, somewhat brittle and requires a lot of know-how to adjust for a new problem. The goal is to have a library that allows to create and configure different deep RL agents with just a few lines, so they are easy to play around with. Development is going slowly because there is a lot of research output that is difficult to integrate into one consistent architecture (also a weekend project), but working prototype with example usage is here: https://github.com/reinforceio/tensorforce