Introduction to Reinforcement Learning.

Revision en1, by bhikkhu, 2023-01-01 10:29:11

We would like to create a model that which when given a game state, it predicts the best move.

Lets say our game is the simple Tic Tac Toe. It is a small game and we can train the AI for it in a handful of minutes.

Here is our example neural network, reduced the number of hidden layer to avoid cluttering.

In the above network, the inputs are going to be board states. For example,

Lets assume the neural networks always predicts from the perspective of that the turn is of player -1.

If we can build a neural network, we can just flip the board and predict for the opposite player, easy peasy.

Tags reinforcement learning, ai, self-play, tic tac toe

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  Rev. Lang. By When Δ Comment
en11 English bhikkhu 2023-01-01 11:05:12 2 Tiny change: 'ke this:\n1. Run a' -> 'ke this:\n\n1. Run a'
en10 English bhikkhu 2023-01-01 11:03:39 3 Tiny change: 'rformance Atari gam' -> 'rformance in Atari gam'
en9 English bhikkhu 2023-01-01 11:02:31 39 Tiny change: 'ation\n — Run a pla' -> 'ation\n --> Run a pla'
en8 English bhikkhu 2023-01-01 11:00:07 7
en7 English bhikkhu 2023-01-01 10:57:01 2 Tiny change: 'radical net way that ' -> 'radical new way that '
en6 English bhikkhu 2023-01-01 10:55:46 73
en5 English bhikkhu 2023-01-01 10:54:33 1786 Tiny change: 'bb94c.png)' -> 'bb94c.png)\n\n' (published)
en4 English bhikkhu 2023-01-01 10:45:44 62
en3 English bhikkhu 2023-01-01 10:41:12 133
en2 English bhikkhu 2023-01-01 10:38:11 958
en1 English bhikkhu 2023-01-01 10:29:11 880 Initial revision (saved to drafts)