Go kotlin, Go!
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Go is an ancient abstract strategy board game intended for two players to surround more territory than the opponent, with its modern rules formalized in Japan during the 15th century. The document discusses the game mechanics, deep learning applications in creating a Go-playing AI, and provides insights into optimization techniques like minimax and Monte Carlo tree search. It also mentions a Kotlin project for developing a Go bot and includes performance timings for various operations.
Go kotlin, Go!
- 1. Go, Kotlin Go! Go is an abstract strategy board game for two players, in which the aim is to surround more territory than the opponent. The game was invented in ancient China more than 2,500 years ago and is believed to be the oldest board game continuously played today. The game reached Japan in the 7th century CE —where it is called go ( 碁 ) or igo ( 囲碁 ). The modern version of the game as we know it today was formalized in Japan in the 15th century.
- 3. AlphaGo Zero
- 4. Rules At the beginning of the game, the board is empty. One player takes the black stones, the other player the white ones. The player with the black stones makes the first move. A move is made by placing a stone on an interesection. A player can play on any unoccupied intersection he wants to. A stone does not move after being played, unless it is captured and taken off the board. The previous board position cannot be recreated.
- 5. Eyes and false eyes There is no special rule for life and death of groups, they just emerge from capture rule. How cool is that? Diagrams from https://senseis.xmp.net/
- 6. Computer Go Nice client https://sabaki.yichuanshen.de/ Strongest Open Source Go program http://pachi.or.cz/ DeepLearning Open Source https://github.com/gcp/leela-zero Strongest commercial Go program https://www.remi-coulom.fr/CrazyStone/ Old generation Open Source https://www.gnu.org/software/gnugo/
- 7. Deep Learning and the Game of Go opens up the world of deep learning and AI by teaching you to build your own Go- playing machine. You'll explore key deep learning ideas like neural networks and reinforcement learning and maybe even step up your Go game a notch or two. AI experts and Go enthusiasts Max Pumperla and Kevin Ferguson take you every step of the way as you build your Go bot and train it from eternal loser to hardened Go player. https://github.com/uberto/kakomu I think that this Python code would look better in Kotlin...
- 8. KAKOMU ( 囲む ) https://github.com/uberto/kakomu In Japanese it means “to surround” and the ideogram is the same that in the game of Go First commit 2nd Jan 2018 42 classes and about 700 loc (excluding tests) It’s able to play at a intermediate level on a 9x9 board Next big feature will be DeepLearning support
- 9. Lessons Learned ● Don’t fear the ? ● Avoid !! at any cost ● Immutability is nice but… ● Algebraic Data Types to avoid if ● Premature Optimization is... ● Kotlin is awesome!
- 10. How to capture? Stone: each position on the board can be empty|white|black String: a set of stones which are in contact Liberties: the empty positions touched by a string Group: a group of strings that “work together”. Computer don’t care about it. Look at code Board.placeStone()
- 12. How to decide who won? A B C D E F G H J 9 . x x x o . o . o 9 8 x x x o o o o o . 8 7 . x x x o o . o o 7 6 x x x o o o o . o 6 5 . x x x x x o o o 5 4 x x x x x o o o . 4 3 x . x . x x o o o 3 2 x x . x x x x x o 2 1 x . x x . x o o o 1 A B C D E F G H J Final score black: 45 white: 36
- 13. Minimax Minimax algorithm with alternate moves. A minimax algorithm is a recursive algorithm for choosing the next move in an n-player game, usually a two-player game. A value is associated with each position or state of the game.
- 14. AlphaBeta pruning The benefit of alpha–beta pruning lies in the fact that branches of the search tree can be eliminated. This way, the search time can be limited to the 'more promising' subtree, and a deeper search can be performed in the same time. The optimization reduces the effective depth to slightly more than half that of simple minimax if the nodes are evaluated in an optimal or near optimal order (best choice for side on move ordered first at each node).
- 15. Monte Carlo Tree Search The focus of Monte Carlo tree search is on the analysis of the most promising moves, expanding the search tree based on random sampling of the search space. The application of Monte Carlo tree search in games is based on many playouts. In each playout, the game is played out to the very end by selecting moves at random. The final game result of each playout is then used to weight the nodes in the game tree so that better nodes are more likely to be chosen in future playouts.
- 16. Performance Timings for a complete rollout from start single thread on my laptop i7 2Ghz (in millisec on 9x9 and 19x19) 10 160 validmove without deepcopy 6 65 immutable goStrings 3.5 38 neighbors map 1.5 31 faster isAnEye 1.3 27 simpleKo 1.25 25 selectMove returning State 0.7 12 remove System.nanotime 0.5 4.5 RandomBot evaluating single move 0.33 2.4 single swap instead of shuffle 0.27 2.1 array instead of map for winCount
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