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Players are now chosen with --sheep/-S and --tiger/-T (human, claude or a
weights file) and --time/-t, instead of by editing main.rs. Every training
setting is a train option, and --help covers all commands.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
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Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
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- train --from <weights> starts from any saved network; without it,
training still carries on from the out dir's newest generation
- Each epoch takes as many positions from sheep-win games as from
tiger-win games (Config::balance_outcomes, on by default)
- Training output shows the error on tiger-win and sheep-win positions
separately, before and after training
- Fix a broken sentence in CLAUDE.md's cache description
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
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- 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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Added benchmarking capabilities
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Now uses ratatui
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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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