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 the concepts clearer.

And a few more resources I found really helpful: http://karpathy.github.io/2016/05/31/rl/ https://www.analyticsvidhya.com/blog/2017/01/introduction-to... https://www.oreilly.com/ideas/reinforcement-learning-with-te...

Edit: My point that I forgot to mention was that I always feel like I am playing catch-up to understand what is going on half the time as the amount of new content being released exceeds what I can absorb.