docs: Add a docs folder on building, the code's structure, the search and trainingHEADv0.1.0master

Covers building and dependencies, the architecture and game loop, the board
representation, the Player and MinimaxPlayer traits, the minimax search,
the network and self-play training, benchmarking, and how to add a player.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

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+# Adding a player
+
+There are two kinds of player to add:
+
+- **A minimax player** — a new evaluation function on top of the existing search. This is the
+  easiest and usually what you want for a new computer opponent. Implement `MinimaxPlayer`.
+- **Any other player** — a different algorithm (Monte Carlo tree search, a random mover, a player
+  over the network, a keyboard-driven human…). Implement `Player` directly.
+
+Then, either way, make it selectable on the command line.
+
+Read [Players](players.md) first for what each trait method is for.
+
+## Option A: a minimax player
+
+Say we want a player that only counts dead sheep and tiger mobility. Create
+`src/greedy-player.rs`:
+
+```rust
+use std::time::Duration;
+
+use crate::{board::{GameBoard, Turn}, player::{Killers, MinimaxPlayer, ScoreCache, SearchReport, TranspositionTable}};
+
+/// A computer player that searches like `ClaudePlayer` but only counts dead sheep and tiger moves.
+pub struct GreedyPlayer
+{
+	pub time_limit: Duration,
+	cache: TranspositionTable,
+	killers: Killers,
+	last_search: Option<SearchReport>,
+}
+
+impl GreedyPlayer
+{
+	pub fn new(time_limit: Duration) -> Self
+	{
+		Self { time_limit, cache: TranspositionTable::new(20), killers: Killers::default(), last_search: None }
+	}
+}
+
+impl MinimaxPlayer for GreedyPlayer
+{
+	/// Positive favours tigers, whichever side is to move.
+	fn evaluate(&mut self, board: &GameBoard, _turn: Turn) -> f32
+	{
+		let dead = 20 - board.sheep_count() - board.sheep_left();
+		100.0 * dead as f32 + board.get_moves(Turn::Tiger).len() as f32
+	}
+
+	fn name(&self) -> String { "Greedy".to_string() }
+
+	fn time_limit(&self) -> Duration { self.time_limit }
+
+	fn cache(&mut self) -> Option<&mut dyn ScoreCache> { Some(&mut self.cache) }
+
+	fn killers(&mut self) -> Option<&mut Killers> { Some(&mut self.killers) }
+
+	fn set_last_search(&mut self, report: Option<SearchReport>) { self.last_search = report; }
+
+	fn last_search(&self) -> Option<SearchReport> { self.last_search }
+}
+```
+
+That's all: the blanket `impl<P: MinimaxPlayer> Player for P` gives it `pick_move` with iterative
+deepening, the "Thinking…" panel and the last-turn report. **Don't also write `impl Player for
+GreedyPlayer`**; it would conflict with the blanket impl.
+
+Things to get right in `evaluate`:
+
+- **Sign.** Positive is good for the tigers, negative good for the sheep, regardless of `turn`.
+- **Finite values.** `±f32::INFINITY` means a won game to the search; an evaluation that returns it
+  would be read as a forced win and stop iterative deepening.
+- **Symmetry.** With a cache, the search shares one entry between the 8 symmetric boards, so
+  `evaluate` should give them the same score. Anything built from counts (pieces, moves, jumps) is
+  symmetric automatically; something like "sheep on the left half" is not.
+- **Speed.** `evaluate` runs at every leaf, which is most of the nodes. `get_moves` allocates a
+  `Vec`; the bitmask accessors (`tiger_points`, `sheep_points`, `sheep_count`) are much cheaper.
+
+`cache`, `killers` and the `last_search` pair are optional. Leave them out and the player still
+works, just searching more slowly and with an empty last-turn panel.
+
+## Option B: any other player
+
+Implement `Player`. A computer player does its work in `pick_move` and returns `Some`. Here is a
+player that moves at random, using the crate's own `Rng` from `network.rs`
+(`src/random-player.rs`):
+
+```rust
+use crate::{board::{GameBoard, Move, Turn}, network::Rng, player::Player};
+
+/// Plays a uniformly random legal move.
+pub struct RandomPlayer
+{
+	rng: Rng,
+}
+
+impl RandomPlayer
+{
+	pub fn new(seed: u64) -> Self
+	{
+		Self { rng: Rng::new(seed) }
+	}
+}
+
+impl Player for RandomPlayer
+{
+	fn name(&self) -> String { "Random".to_string() }
+
+	fn pick_move(&mut self, board: GameBoard, turn: Turn, _previous: &[GameBoard]) -> Option<Move>
+	{
+		// pick_move is only called while the game is on, so there is always a move
+		let moves = board.get_moves(turn);
+		Some(moves[self.rng.below(moves.len())])
+	}
+}
+```
+
+Things to keep in mind:
+
+- **Return only legal moves.** The coordinator `unwrap`s `make_move`, so an illegal move panics.
+  Pick from `board.get_moves(turn)`, or check with `board.check_move(mv)`.
+- **Don't block forever.** The coordinator can't redraw or react to `q` while `pick_move` runs. Input
+  that arrives meanwhile is handled as soon as it returns. Keep a time limit.
+- **Repetitions.** `previous` holds every earlier board in the game. `board::repetitions(previous,
+  &next)` says how often a position came up before; `REPETITION_DRAW` (2) earlier occurrences make a
+  draw.
+- **Interactive players** return `None` from `pick_move` and produce their move from
+  `handle_input`, as `LocalPlayer` does. Use `update` to remember the position for `render`,
+  `highlights` to show a selection, and clear your own selection when you return a move. `reset`
+  is called after the user reverts, so drop any selection there too.
+- **Panels.** Override `render` for the panel shown on your turn and `render_last_turn` for comments
+  on your last move, shown during the opponent's turn. Both draw with ratatui into the given `area`
+  of `buf`, e.g. `Paragraph::new(text).render(area, buf)` (with `ratatui::widgets::Widget` in scope).
+
+## Wiring it into the command line
+
+All in [`src/main.rs`](../src/main.rs).
+
+**1. Declare the module.** File names with a hyphen need `#[path]`:
+
+```rust
+#[path = "greedy-player.rs"]
+mod greedy_player;
+```
+
+and import the type: `use crate::greedy_player::GreedyPlayer;` (or add it to the existing
+`use crate::{…}` list).
+
+**2. Add a `PlayerKind` variant:**
+
+```rust
+enum PlayerKind
+{
+	Human,
+	Claude,
+	Greedy,
+	Neural(PathBuf),
+}
+```
+
+**3. Parse its name** in `parse_player`, before the file-path case, and mention it in the error:
+
+```rust
+"greedy" => Ok(PlayerKind::Greedy),
+…
+_ => Err(format!("expected `human`, `claude`, `greedy` or a weights file, but `{s}` is none of these")),
+```
+
+**4. Construct it** in `make_player`:
+
+```rust
+PlayerKind::Greedy => Box::new(GreedyPlayer::new(time_limit)),
+```
+
+**5. Update the help text** on the `sheep` and `tiger` fields of `PlayArgs` (their doc comments are
+what `--help` shows), and the player list in the top-level `README.md`.
+
+Then:
+
+```bash
+cargo run --release -- -S human -T greedy
+cargo run --release -- -S greedy -T claude --time 1
+```
+
+If the new type is only used from `main.rs` through `make_player`, everything is reachable and there
+are no dead-code warnings. A constructor or field that nothing uses yet will warn; that's a sign to
+either use it or remove it.
+
+## Testing it
+
+Put tests in a `#[cfg(test)] mod tests` at the bottom of the player's file, as the other players do.
+Useful checks:
+
+- `pick_move` from a few positions returns a move with `board.check_move(m) == Ok(())`.
+- For a minimax player, `last_search()` reports the move it returned.
+- A player with a clear preference picks the obvious move: `player.rs` has a position where one
+  tiger jump wins the game (`tiger_takes_the_winning_kill`) that is easy to copy.
+
+To see how it plays, play it against `ClaudePlayer` in the TUI with both sides computer-controlled:
+
+```bash
+cargo run --release -- -S greedy -T claude
+```
+
+For a proper strength comparison over many games, `train.rs`'s `match_score` and `play` show how to
+run games without the TUI: `play` takes a closure that returns `(move, score)` for a position, so
+any `Player` can be plugged in.
+
+## Using it in training
+
+`train.rs` plays its matches against `ClaudePlayer` (`claude(depth)`). To measure the network
+against your player instead, add a constructor like `claude` for it and pass it to `match_score`,
+which accepts any `MinimaxPlayer + Player`. Keep the match opponents out of the training data:
+they are a yardstick, and the network is meant to learn from self-play alone.