| Commit message (Collapse) | Author | Age | Files | Lines | |
|---|---|---|---|---|---|
| * | feat: Multi-threaded training; play matches every 10 generations | Dylan | 18 hours | 1 | -3/+85 |
| | | | | | | | | | | | | | Gradients and losses are computed on rayon's pool in fixed-size chunks summed in order, so the weights are the same on any number of threads. Self-play uses the same pool, and the output shows the training time. The matches against the earlier network and ClaudePlayer now run only every --match-every generations (default 10), against the generation that last played them, read back from its saved file. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> | ||||
| * | feat: Neural network opponent trained by self-play | Dylan | 5 days | 1 | -0/+425 |
| - NeuralPlayer: a MinimaxPlayer that scores positions with a small MLP (network.rs, hand-written backprop and Adam, no new dependencies) - train subcommand: self-play with TD(lambda) targets, cut at random exploration moves so sheep blunders don't bias the net towards tigers - neural subcommand: play against a trained network - evaluate now gets the side to move - Board symmetries; the search cache keys by the canonical board, which halves the depth-9 bench time with identical moves and scores Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> | |||||
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