4 ms·

Really cool! How much work would it be to get this to work for chess?

brilee 9y ago
You'd have to write a Chess implementation that very carefully respects all possible game-ending pathways, a translation of a chess board into an array that a NN could understand, and a schema by which to flatten the array of all possible moves (both legal and illegal) into a single vector. Then, the MCTS and RL portions would be identical.
- jimmytucson 9y ago
  Sounds like this meets the requirements: https://github.com/niklasf/python-chess https://github.com/niklasf/python-chess > very carefully respects all possible game-ending pathways Not sure exactly what this one means but it implements a is-game-over method, so you could do MCTS with it. > a translation of a chess board into an array that a NN could understand Chess positions are represented by sets of 64-bit integers so I don’t think this would be a blocker. > and a schema by which to flatten the array of all possible moves (both legal and illegal) into a single vector The list of legal moves is a set of bitboards as well.
  - dsjoerg 9y ago
    Not quite. python-chess is a wonderful library, and you would use it to do the things that your parent said. But python-chess on its own doesn't do those things.
glinscott 9y ago
Check out https://github.com/glinscott/leela-chess https://github.com/glinscott/leela-chess. We are getting close to kicking off the distributed version now that we have validated it's possible to get a strong network through supervised training. A nice win against Gnuchess (a very weak opponent, but nonetheless :) - https://github.com/glinscott/leela-chess/issues/47#issuecomment-361174111 https://github.com/glinscott/leela-chess/issues/47#issuecomm...
- grondilu 9y ago
  > A nice win against Gnuchess Not sure why you guys don't show the PGN, but here you are: 1. e4 Nc6 2. Nf3 Nf6 3. Nc3 d5 4. exd5 Nxd5 5. Bb5 Nf4 6. O-O Bf5 7. d4 Nd5 8. Ne5 Qd6 9. Nxd5 Qxd5 10. Bxc6+ bxc6 11. c4 Qd6 12. Qf3 g6 13. Nxc6 Bg7 14. Bf4 Qe6 15. Rfe1 Qxc4 16. Rxe7+ Kf8 17. Rae1 Kg8 18. b3 Qc2 19. Qd5 Be6 20. R7xe6 fxe6 21. Qxe6+ Kf8 22. Bh6 Bxh6 23. Qf6+ Kg8 24. Ne7# Question : when you switch to self-play reinforcement learning, do you plan on starting from the networked obtained in supervised learning or tabula rasa? I understand starting from tabula rasa will require more comptuting power/time, but if you start from the supervised learning network, isn't there a risk you inherit human biases in the game style? It would also defeat the purpose of having the system discover existing chess theory and possibly new one.
  - glinscott 9y ago
    We are going to start tabula rasa. The supervised learning is meant to prove that there are no major bugs in the framework/learning process. Should be fun to watch it learn chess theory :).

You'd have to write a Chess implementation that very carefully respects all possible game-ending pathways, a translation of a chess board into an array that a NN could understand, and a schema by which to flatten the array of all possible moves (both legal and illegal) into a single vector. Then, the MCTS and RL portions would be identical.

jimmytucson 9y ago
Sounds like this meets the requirements: https://github.com/niklasf/python-chess https://github.com/niklasf/python-chess > very carefully respects all possible game-ending pathways Not sure exactly what this one means but it implements a is-game-over method, so you could do MCTS with it. > a translation of a chess board into an array that a NN could understand Chess positions are represented by sets of 64-bit integers so I don’t think this would be a blocker. > and a schema by which to flatten the array of all possible moves (both legal and illegal) into a single vector The list of legal moves is a set of bitboards as well.
- dsjoerg 9y ago
  Not quite. python-chess is a wonderful library, and you would use it to do the things that your parent said. But python-chess on its own doesn't do those things.

glinscott 9y ago

Check out https://github.com/glinscott/leela-chess https://github.com/glinscott/leela-chess. We are getting close to kicking off the distributed version now that we have validated it's possible to get a strong network through supervised training. A nice win against Gnuchess (a very weak opponent, but nonetheless :) - https://github.com/glinscott/leela-chess/issues/47#issuecomment-361174111 https://github.com/glinscott/leela-chess/issues/47#issuecomm...

grondilu 9y ago
> A nice win against Gnuchess Not sure why you guys don't show the PGN, but here you are: 1. e4 Nc6 2. Nf3 Nf6 3. Nc3 d5 4. exd5 Nxd5 5. Bb5 Nf4 6. O-O Bf5 7. d4 Nd5 8. Ne5 Qd6 9. Nxd5 Qxd5 10. Bxc6+ bxc6 11. c4 Qd6 12. Qf3 g6 13. Nxc6 Bg7 14. Bf4 Qe6 15. Rfe1 Qxc4 16. Rxe7+ Kf8 17. Rae1 Kg8 18. b3 Qc2 19. Qd5 Be6 20. R7xe6 fxe6 21. Qxe6+ Kf8 22. Bh6 Bxh6 23. Qf6+ Kg8 24. Ne7# Question : when you switch to self-play reinforcement learning, do you plan on starting from the networked obtained in supervised learning or tabula rasa? I understand starting from tabula rasa will require more comptuting power/time, but if you start from the supervised learning network, isn't there a risk you inherit human biases in the game style? It would also defeat the purpose of having the system discover existing chess theory and possibly new one.
- glinscott 9y ago
  We are going to start tabula rasa. The supervised learning is meant to prove that there are no major bugs in the framework/learning process. Should be fun to watch it learn chess theory :).