Tigers and Sheep documentation

These pages explain how the game is built and how its computer players think. The top-level README covers the rules and how to play; start here when you want to change the code.

Page	What it covers
Building	Toolchain, dependencies and what each is for, Cargo profiles and features, the Nix flake, CI
Architecture	The modules, how a game runs from `main` to the TUI loop, and how the pieces talk to each other
The board	The bitboard representation, move generation, the rules as implemented, symmetries and repetitions
Players	The `Player` and `MinimaxPlayer` traits, and the players that implement them
The minimax search	Alpha-beta, iterative deepening, the transposition table, move ordering, repetitions
The neural network and training	The network, backprop and Adam, and the self-play training loop with TD(λ) targets
Benchmarking and profiling	The `bench` command, search counters, `perf`/`samply`, and pitfalls when timing
Adding a player	Step by step: a simple player, a minimax player, and wiring either into the command line

Conventions used throughout

Scores favour tigers. Every score in the code, from evaluate to the network's output, is positive when it's good for the tigers and negative when it's good for the sheep. Tigers maximise, sheep minimise.
Coordinates. A point is Pos = (x, y), with x and y in 0..5. y = 0 is drawn at the top. In notation, files a–e are x 0–4 and ranks are 5 - y, so (0, 0) is a5.
Move notation. sc3 places a sheep on c3, sc3c2 moves a sheep, ta5b4 is a tiger step and ta5xc3 a tiger jump (which kills the sheep on b4).
Turns. Sheep move first. Turn is the side about to move, and most functions take it alongside the board, since the same board can come up with either side to move.