thejayman77
479be1f6b9
Kotlin Multiplatform engine (JVM target only for now; androidTarget and iosArm64 slot in without touching commonMain). Core: - HandEvaluator: single-pass 5-7 card evaluation, ~24M evals/sec. Verified exhaustively against published frequencies for all 2,598,960 five-card hands. - Equity: Monte Carlo with ties split. PreflopChart ranks the 169 starting hands using all-in equity plus an explicit playability adjustment, so looseness means "plays the top N%". - Table: no-limit betting rounds, side pots, odd-chip splits, uncalled-bet refunds, and incomplete (short all-in) raises that correctly do not reopen betting. Bots: - SkillLevel and PlayStyle are orthogonal axes. Skill drives decision quality (rollout accuracy, pot-odds discipline, position awareness, error rate); style drives bluffing, sandbagging, aggression, tightness. - BotMood gives tilt that persists between hands and decays. - OpponentModel lets Advanced/Expert exploit habitual bettors. - MathBot emits a DecisionTrace of the numbers behind each decision, which the coach will later hand to an LLM to narrate. The LLM never does poker maths. Simulator: - 2,200-3,400 hands/sec. Deck RNG is separate from bot RNGs so rollout counts cannot shift the deal. - Controlled skill-ladder test asserts the difficulty gradient is monotonic: 73.9 / 53.9 / 27.6 / -155.4 bb/100 over 50k hands. Assets: 52 CC0 English-pattern card faces plus generated backs. Tests: 30 passing (evaluator, table rules, pre-flop chart). Known open: win-rate magnitudes ~10x realistic and several profiles looser than their labels. Tuning, not correctness. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
69 lines
3.1 KiB
Markdown
69 lines
3.1 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; side pots, odd-chip splits, uncalled-bet refunds, and
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incomplete (short all-in) raises all covered by tests.
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- Bots: skill gradient **passes** monotonically (73.9 / 53.9 / 27.6 / −155.4
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bb/100 at 50k hands).
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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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