| Commit message (Collapse) | Author | Age | Files | Lines | |
|---|---|---|---|---|---|
| * | feat: Neural network opponent trained by self-play | Dylan | 5 days | 1 | -0/+211 |
| | | | | | | | | | | | | | | - 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> | ||||
| * | feat: Optimisation | Dylan | 2026-10-23 | 1 | -28/+72 |
| | | | | | Added benchmarking capabilities | ||||
| * | feat: Improvements to UI | Dylan | 2026-10-22 | 1 | -2/+58 |
| | | | | | Now uses ratatui | ||||
| * | feat: Initial commit | Dylan | 2026-10-19 | 1 | -0/+1149 |
| What works: - Minimax search - With a simple player claude designed - Manual player - Move generation - Board display What needs improvement: - Manual player - Game coordinator - Draw checking - Nix setup | |||||
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index : boss/tigers-and-sheep.git | |
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