[mks40]

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