AI Poker Bot - Jump Trading Poker Competition
Jump Trading x CMU AI Poker Tournament 2026
Built a competitive AI poker agent for the Jump Trading x CMU AI Poker Tournament 2026, competing against 100+ teams in an ELO-ranked tournament. The game is a modified Texas Hold'em variant played on a 27-card deck (ranks 2-9 and Ace, three suits only) with a unique discard mechanic where players receive 5 hole cards, discard 3 after the flop, and discards are revealed to the opponent.
System Architecture
Entry Point
PlayerAgent
Input
State Parser
Game state decoding
Intel
Opponent Tracker
Bayesian profiling
Safety
Bankroll Manager
Lock-in logic
Strategy Engine
Core Engine
Opponent Classification
Bayesian profiling classifies opponents in real-time, adjusting strategy thresholds dynamically.
Nit
VPIP < 20%
Bluff more
TAG
VPIP 20-30%
Respect raises
LAG
VPIP 30-45%
Trap & call
Station
VPIP 45-60%
Value bet heavy
Maniac
VPIP > 60%
Let them hang
Decision Pipeline
Pre-flop Hand Scoring
Score 5-card hands via pairs/trips bonus, suit concentration, connectivity, high cards, and flexibility. Map to percentile from 80,730 precomputed rankings.
Position-Aware Action Selection
Separate thresholds for Small Blind (discards 2nd, info advantage) and Big Blind (discards 1st, info disadvantage). Adjusts raise sizing and frequency by position.
Discard Phase
Select optimal 2-card keep from 5 hole cards. Evaluate each of 10 combinations via equity against estimated opponent range, penalizing information leakage.
Post-flop Equity-Based Betting
Compute real-time equity against Bayesian-weighted opponent range. Exploit calling stations with value bets, bluff nits more aggressively.
Bankroll Lock-in
When sufficiently ahead, calculate worst-case blind losses for remaining hands and lock in the win by folding everything - a mathematically guaranteed victory condition.
Equity Computation Approach
Exact Enumeration
Enumerate all possible opponent hands and board completions. Precise but computationally expensive.
Used for: discard phase, small remaining decks
Monte Carlo Sampling
Random sampling with configurable iteration limits. Fast approximation for time-critical decisions.
Used for: post-flop betting, large search spaces
Discard Optimization
5
Hole Cards
10
Possible Keeps
2
Cards Kept
Heuristic scoring of all 10 keeps (pair quality, flush potential, straight draws)
Top-k filter narrows to best 3-4 candidates
Exact equity evaluation of top candidates against opponent range
Information leak penalty - discards are revealed, so penalize keeps that leak hand strength
Tournament Format
Jump Trading x CMU AI Poker Tournament 2026
Open Season
March 14-21, 2026
ELO-ranked matchmaking, ~116 matches/day
Finals
March 22, 2026
Top 10 teams, 1000-hand matches
Compute Phases
3 Phases
1 vCPU / 500s → 4 vCPU / 1500s
Iteration History
Basic hand evaluation and simple threshold-based betting. Established the agent framework and game engine integration.
Added exact equity computation and precomputed hand lookup tables. Implemented heuristic-based discard selection.
Introduced opponent tracking with 12+ metrics and dynamic strategy adjustment. Added Bayesian range weighting from revealed discards.
Monte Carlo sampling for speed, modular strategy architecture (preflop/postflop/discard/exploit), NumPy-based tracking, and bankroll lock-in logic.