[robinson_k]

Enrolled! Went through the detailed lesson plan and you have done a great job structuring the course. I am looking forward to doing it over the weekend.

One suggestion: Instead of naming all the Jupyter notebooks "coding_exercise.ipynb", maybe name them differently? That way, they won't overwrite the previous download.

[gh1]

Good catch. I can imagine that this is annoying. I have put it in my todo.

I hope you enjoy the course over the weekend.

[pciexpgpu]

Thank you for doing this!

I haven't looked deeply enough, but does this course use a higher-level 'package' such as OpenAI Gym or teach at a lower-level? (Is lower-level stuff even possible...)

[gh1]

I think the levels (high, low etc.) are relevant for the Deep RL algorithm, not the environment. The lower level version of OpenAI Gym canned environments would be custom Gym environments. I don't see much reason to go any lower than that.

The situation looks different for Deep RL algorithm. You can implement them from scratch yourself using Tensorflow or any other similar library. Otherwise, you could just use a higher-level library like RLlib which implements the algorithm using modular components and exposes hyperparameters as configuration parameters.

In many real world use cases, all one needs to do is to use RLlib's implementation and then tune the hyperparameters. In that way RLlib is to Deep RL what Keras is to Deep Learning.

This course uses RLlib. Does that answer your question?

[pciexpgpu]

Great, yep, that is good to know.