As I discussed before, there are not at many good reinforcement learning material out there. But I found this great set of tutorial and I will share my journey of learning it.

Start reading these two set of of blogs,

1. Blog Number 1
2. Blog Number 2

At the end of the second blog, you will find jupyter notebook for the Frozen Lake tutorial.

https://github.com/simoninithomas/Deep_reinforcement_learning_Course/blob/master/Q%20learning/FrozenLake/Q%20Learning%20with%20FrozenLake_unslippery%20(Deterministic%20version).ipynb

Before you start the tutorial, you will like need to learn how the Gym environment works. Go to this link and read the super basic tutorial they have there. Note especially what are the component of each episode. Actually figure out what are the possible actions, and what does each value of the state means.

http://gym.openai.com/docs/

Here is the wiki for the basic parameters. https://github.com/openai/gym/wiki/CartPole-v0

Refer to the source for what “Discrete” and “Box” are. https://github.com/openai/gym/tree/master/gym/spaces

Run the code on Google Colab and see how it runs. Print out the variable for each episode and step. I made an example in case you want to follow. https://colab.research.google.com/drive/1oqon14Iq8jzx6PhMJvja-mktFTru5GPl

Run the deterministic state first and then stochastic. Now you know how to create the super basic, Q table.