140 lines
8.1 KiB
Markdown
140 lines
8.1 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 # engine tests (JVM)
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./gradlew :engine:testAndroidHostTest # same suite, Android variant
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./gradlew :sim:run --args="50000" # simulate 50k hands, print bot stats
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./gradlew :sim:test # fast calibration-policy tests
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./gradlew :sim:run --args="styles" # enforced controlled style experiment
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./gradlew :sim:run --args="calibrate" # enforced 4×100k fixed-pool skill + style calibration
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./gradlew :sim:run --args="short-stacks" # persistent stacks; asserts side-pot/call-clamp coverage
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./gradlew :app:assembleDebug # build the APK
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```
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## Layout
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| Path | What |
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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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| `app/` | Android app: Compose table, `PokerViewModel` |
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| `assets/cards/` | 52 CC0 card faces + generated backs (**source of truth**) |
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| `tools/generate_card_assets.sh` | Rasterises those SVGs into `app/.../drawable-*` |
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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, across
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several seeds — a single seed will happily agree with a wrong conclusion.
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The quick 50k run is diagnostic; only `calibrate` is an enforced result.
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Its paired skill comparisons reset equal stacks to isolate skill, then a
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mandatory persistent-stack pass verifies that short-stack pricing is live.
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5. **A skill parameter must not smuggle in a style change.** Several bugs came
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from exactly this: `positionAwareness` silently reduced hands played,
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`potOddsRespect` systematically loosened weak players (which is a *winning*
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adjustment, so it inverted the gradient), and error direction overwrote style
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entirely. Skill should change how *well* a decision is made, not how loose or
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tight the player is.
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6. **Pot odds are the post-flop baseline.** Never apply a blanket implied-odds
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discount: it is categorically wrong on the river, and future value on earlier
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streets must account for future costs and reverse implied odds before it is
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called an advantage. Price a call against `eligiblePot`, not the displayed
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total: a short stack cannot win side-pot chips above its contribution level.
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7. **DecisionTrace is the coach contract.** It records raw pot odds, the actual
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adjusted threshold, every adjustment, intended and chosen actions, and whether
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a skill error changed the decision. Pre-flop chart percentile/range fields are
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structurally separate from post-flop equity fields; inapplicable values are
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null. The production coach explains these values; it does not reconstruct
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hidden bot logic.
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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 the paired
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4×100k fixed-opponent calibration before accepting a skill change; it computes
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confidence bounds and exits nonzero when the contract fails.
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- Equal starting stacks are deliberate in the paired skill experiment, but they
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cannot exercise side-pot pricing. `calibrate` therefore finishes with 3,000
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persistent-stack hands and fails unless both `eligiblePot != pot` and
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`toCall > stack` occur. `:sim:test` pins the same paths at a fixed seed.
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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: controlled style calibration holds style constant against the same five
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opponents and deal seed. Rock is 10.5% VPIP, Maniac 67.4%; looseness ordering,
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Calling Station passivity (10.0% PFR, 0.27 AF), Maniac aggression, and PFR
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relationships all pass.
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- Skill calibration pairs four 100k-hand seeds. Every candidate occupies the same
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seat against the same fixed opponent pool and deal seed. Advanced and Expert
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are allowed to overlap, but both must beat Intermediate and Intermediate must
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beat Beginner with a positive 95% lower confidence bound. Current lower bounds
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are +24.42, +11.01, and +24.67 bb/100 respectively.
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- Persistent-stack calibration currently reaches 351 decisions with inaccessible
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side-pot chips and stack-clamped calls in 3,000 hands (seed 20260729).
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- Expert differs by mechanism: it alone maintains opponent reads. The aggression
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prior is measured by the controlled neutral TAG experiment (0.231 observed,
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0.22 configured), not selected because it looks plausible.
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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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- `TableSnapshot.lastAction` intentionally survives board-transition frames.
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Live status copy may carry it into the next decision only when the event's
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`street` matches the snapshot, or the first flop actor resurrects a pre-flop
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action.
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- Android is playable in Compose. Engine snapshots are paced through a
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rendezvous channel, human decisions are matched by engine-owned tokens, and a
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completed hand stays on screen until the player explicitly starts the next one.
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- A cash-game session begins at an explicit take-a-seat screen. Opponents may
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auto-reload below the big blind; the human is never silently topped up and must
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explicitly choose "Reload to N & deal" from the completed-hand screen.
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- The deterministic coach is opt-in and post-action only. Its fundamentals
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baseline receives `DecisionOffer`, so it can see the hero's cards and public
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table state but no opponent hole cards. Post-flop equity is explicitly labelled
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as an estimate against unknown random hands; disagreement is "worth reviewing,"
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never declared solver proof of a mistake.
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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.
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