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>
112 lines
6.9 KiB
XML
112 lines
6.9 KiB
XML
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