[qlk1123]

(Edit for fixing the format)

Surprised to know nobody mentions reinforcement learning here.

Bought three books (in their transitional Chinese edition), whose original titles are,

* Reinforcement Learning 2nd, Richard S. Sutton & Andrew G. Barto

* Deep Reinforcement Learning in Action, Alexander Zai & Brandon Brown

* AlphaZero 深層学習・強化学習・探索 人工知能プログラミング実践入門, 布留川英一

None of them teaches you how to apply RL libraries. The first is a text book and mentions nothing about how to use frameworks at all. The last two are more practice oriented, but the examples are both too trivial, compared to a full boardgame, even the rule set is simple for humans.

Since my goal is eventually to conquer a boardgame with an RL agent that is trained at home (hopefully), I would say that the 3rd book is the most helpful one.

But so far my progress has been stuck for a while, because obviously I can only keep trying the hyperparameters and network architecture to find what the best ones for the game are. I kind of "went back" to the supervised learning practice in which I generated a lot of random play record, and them let the NN model at least learn some patterns out of it. Still trying...

[PartiallyNerdy]

You should take a look at OpenAI's spinning up RL! I found it was a great resource when I began studying for RL.

[1] https://spinningup.openai.com/en/latest/