thejayman77
f1222401fb
TDA Rule 47 — cumulative incomplete raises: A boolean could not express "facing at least a full raise since acting", so several short all-ins that together reached a full raise failed to reopen betting. Seat now records lastActedAtBet (the currentBet when it last acted); mayRaise() reopens when currentBet - lastActedAtBet >= minRaiseSize. This subsumes the single-incomplete-raise case, so the hasActed reset in apply() is gone. TDA Rule 20 — odd chips: Split-pot remainders were awarded in seat-list order. They now go to the first winning seat clockwise from the button. The old test also never produced an odd pot (20 chips heads-up), so it only ever proved an even split; it now builds a genuinely odd 25-chip pot via a folded small blind and asserts which seat takes the extra chip. OpponentModel skipped events across hands: It inferred a new hand from a shrinking history, but history is cleared each hand: having consumed 3 events, first observing the next hand at 4 events left 4 < 3 false and silently dropped the first three. observe() now takes an explicit handNumber, exposed via DecisionContext and Table.handNumber. PreflopChart percentile semantics: The 169 classes were ranked equally, but they are not equally likely — a pair is 6 of 1326 combinations, suited 4, offsuit 12. "Top 12%" therefore meant 12% of classes, not of dealt hands, so looseness did not mean what it claimed. Percentiles are now weighted by combination count. Tests: 30 -> 37. Each new test was verified to fail with its fix reverted. Skill gradient still monotonic: 85.9 / 79.8 / 17.4 / -183.1 bb/100 over 50k hands, chips conserved on both tables. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
83 lines
3.9 KiB
Markdown
83 lines
3.9 KiB
Markdown
# Poker — Texas Hold'em with teachable AI opponents
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Kotlin Multiplatform. Ships iOS + Android; Android first (only Android hardware
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for physical testing).
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## Build
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No `java`/`gradle` on PATH — use Android Studio's bundled JDK:
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```bash
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export JAVA_HOME="/Applications/Android Studio.app/Contents/jbr/Contents/Home"
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./gradlew :engine:jvmTest # evaluator + engine tests
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./gradlew :sim:run --args="50000" # simulate 50k hands, print bot stats
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```
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## Layout
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| Path | What |
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|---|---|
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| `engine/src/commonMain/.../core/` | Cards, evaluator, equity, pre-flop chart |
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| `engine/src/commonMain/.../bot/` | Skill/style profiles, `MathBot` |
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| `engine/src/commonMain/.../game/` | `Table` — betting rounds, side pots, showdown |
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| `sim/` | JVM-only headless simulator used to **tune** bot profiles |
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| `assets/cards/` | 52 CC0 card faces + generated backs |
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`engine` is pure Kotlin with no platform APIs, so `androidTarget()` /
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`iosArm64()` slot in without touching `commonMain`.
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## Design rules
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1. **Poker maths never goes near the LLM.** Difficulty and style are engine-side
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EV/frequency calculations — instant, deterministic, testable, offline. The LLM
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only narrates numbers the engine already computed (`DecisionTrace`), and adds
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persona/table talk.
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2. **Skill and style are orthogonal.** `SkillLevel` = how correct decisions are;
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`PlayStyle` = bluffing, sandbagging, aggression, tightness. Build the strongest
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bot, then inject *controlled error* for lower tiers.
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3. **Pre-flop is range-based, not equity-based.** All-in equity overvalues trash
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(7-2o has ~35% vs one random hand but is unplayable). `PreflopChart` ranks the
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169 starting hands so `looseness` means "plays the top N%".
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4. **The simulator is how bots get tuned.** Run it after any bot change; it prints
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a controlled skill-ladder test that must stay monotonic.
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## Testing notes
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- `Table` takes a `CardSource`, so `StackedDeck.of(holes, board)` gives fully
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deterministic hands. Use it for any rule test.
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- The simulator gives the **deck its own RNG**, separate from each bot's. Never
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share one: bots consume RNG proportional to their `equityIterations`, so a
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shared stream means changing a profile silently changes the cards dealt.
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- `./gradlew :sim:run --args="chart"` dumps the starting-hand ranking.
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- Small samples lie. 1,000 hands is not enough to rank profiles — use 50,000+
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before believing a gradient.
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## Status
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- Evaluator: verified exhaustively against published frequencies for all
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2,598,960 five-card hands. ~24M evals/sec.
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- Engine: chip-conserving. Covered by tests: side pots, uncalled-bet refunds,
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action order, malformed agent output, **TDA Rule 47** (incomplete raises do not
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reopen betting, but several that cumulatively reach a full raise do), and
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**TDA Rule 20** (odd chip to the first winner left of the button).
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- Bots: skill gradient **passes** monotonically (85.9 / 79.8 / 17.4 / −183.1
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bb/100 at 50k hands).
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### Rules invariants that are easy to get wrong
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- Reopening betting cannot be a boolean. `Seat.lastActedAtBet` records the bet
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level a player last acted at; betting reopens when `currentBet - lastActedAtBet
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>= minRaiseSize`. Several short all-ins can reach that together.
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- `PreflopChart` percentiles are weighted by **combination counts** (pair 6,
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suited 4, offsuit 12, total 1326), so "top 12%" means 12% of *dealt hands*, not
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12% of the 169 classes.
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- Anything consuming `DecisionContext.history` across hands must key off
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`handNumber`. History is cleared each hand, so a size comparison silently drops
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events.
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- Known-imperfect: win-rate magnitudes are still ~10x realistic, and several
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profiles are looser than their labels (the Rock plays ~38% VPIP, should be
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~12%). Tuning is the open work.
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- Gradle emits an `archives` deprecation from the Kotlin Multiplatform plugin's
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own `jvm()` target registration — upstream in Kotlin 2.2.10, not our build.
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- Not built yet: LLM persona layer, opt-in coach, Compose UI.
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