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...
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...