一、撲克算法整體架構
系統模塊劃分
python
class PokerAI:
def __init__(self):
self.hand_evaluator = HandEvaluator
self.odds_calculator = OddsCalculator
self.decision_engine = DecisionEngine
self.opponent_modeler = OpponentModeler
def make_decision(self, game_state):
綜合分析并做出決策
hand_strength = self.evaluate_hand_strength(game_state)
win_probability = self.calculate_win_probability(game_state)
opponent_tendency = self.model_opponents(game_state)
return self.decision_engine.optimize_decision(
hand_strength, win_probability, opponent_tendency, game_state
二、核心算法解析
1. 手牌強度評估算法
python
class HandEvaluator:
def evaluate_preflop(self, hole_cards):
翻前手牌評估
card1, card2 = hole_cards
# 高牌價值
high_card_value = max(card1.rank_value, card2.rank_value)
# 對子檢測
is_pair = card1.rank == card2.rank
# 同花潛力
suited = card1.suit == card2.suit
# 連牌潛力
gap_size = abs(card1.rank_value
score = self.calculate_preflop_score(
high_card_value, is_pair, suited, gap_size
return score
def calculate_preflop_score(self, *factors):
綜合評分
base_score = 0
# Chen公式簡化版
if factors[1]: # 對子
base_score = max(factors[0] * 2, 5)
else:
base_score = factors[0] # 高牌
if factors[2]: # 同花
base_score += 2
# 間隔調整
gap_penalty = factors[3]
base_score -= gap_penalty
return min(base_score, 20) # 歸一化到0-20
2. 勝率計算引擎
python
class MonteCarloOddsCalculator:
def calculate_equity(self, hole_cards, community_cards, opponents_count, iterations=10000):
蒙特卡洛模擬計算勝率
wins = 0
ties = 0
for _ in range(iterations):
# 生成未知牌
deck = self.generate_deck(hole_cards, community_cards)
remaining_community = self.draw_remaining_community(deck, community_cards)
# 為對手發牌
opponent_hands = []
for _ in range(opponents_count):
opp_hand = [deck.pop, deck.pop]
opponent_hands.append(opp_hand)
# 完整牌面
悟空德州俱乐部full_board = community_cards + remaining_community
# 比較牌力
my_strength = self.evaluate_hand_strength(hole_cards + full_board)
opp_strengths = [self.evaluate_hand_strength(hand + full_board)
for hand in opponent_hands]
if all(my_strength > opp_strength for opp_strength in opp_strengths):
wins += 1
elif any(my_strength == opp_strength for opp_strength in opp_strengths):
ties += 1
equity = (wins + ties/2) / iterations
return equity
3. 決策 決策優化算法
python
class DecisionEngine:
def __init__(self):
self.strategy_matrix = self.load_strategy_matrix
def optimize_bet_sizing(self, game_state, hand_strength):
優化下注尺度
pot_size = game_state.pot_size
stack_depth = game_state.stack / pot_size
# 基于手牌強度和籌碼深度的下注尺度
if hand_strength > 0.8: # 強牌
if stack_depth
return pot_size * 0.75 # 標準下注
else:
return pot_size * 0.6 # 小一點建立底池
elif hand_strength > 0.4: # 中等牌力
return pot_size * 0.5 # 控制底池
else: # 弱牌或詐唬
return pot_size * 0.7 # 較大的詐唬下注
def calculate_fold_equity(self, bet_size, pot_size, opponent_fold_frequency):
計算棄牌率收益
fold_equity = opponent_fold_frequency * pot_size
risk_amount = bet_size
expected_value = fold_equity
return expected_value
三、機器學習增強策略
神經網絡對手建模
python
import torch
import torch.nn as nn
class OpponentModel(nn.Module):
def __init__(self, input_dim=50, hidden_dim=128):
super.__init__
work = nn.Sequential(
nn.Linear(input_dim, hidden_dim),
nn.ReLU,
nn.Linear(hidden_dim, hidden_dim//2),
nn.ReLU,
nn.Linear(hidden_dim//2, 3) # 預測fold/call/raise頻率
def forward(self, features):
預測對手行為
return torch.softmax(work(features), dim=-1)
class RLDecisionAgent:
def __init__(self):
self.q_network = self.build_q_network
self.memory = ReplayMemory(10000)
def build_q_network(self):
構建Q-learning網絡
return tf.keras.Sequential([
tf.keras.layers.Dense(256, activation='relu', input_shape=(STATE_DIM,)),
tf.keras.layers.Dense(128, activation='relu'),
tf.keras.layers.Dense(64, activation='relu'),
tf.keras.layers.Dense(ACTION_SPACE_SIZE) # fold, call, raise
])
def select_action(self, state, epsilon=0.1):
ε-greedy策略選擇動作
if random.random
return random.choice(ACTIONS)
else:
q_values = self.q_network.predict(state[np.newaxis])
return np.argmax(q_values[0])
四、關鍵性能優化技術
1. 快速手牌評估
c++
// C++優化版本手牌評估
uint32_t evaluate_hand_fast(const Card* cards, int count) {
uint64_t key = generate_hand_key(cards, count);
return lookup_table[key]; // 預計算查找表
2. 并行蒙特卡洛模擬
python
from concurrent.futures import ThreadPoolExecutor
def parallel_monte_carlo(hole_cards, board, opponents, total_sims=100000):
并行化勝率計算
sims_per_thread = total_sims // 4
with ThreadPoolExecutor(max_workers=4) as executor:
futures = [
executor.submit(monte_carlo_simulation,
hole_cards, board, opponents, sims_per_thread)
for _ in range(4)
results = [f.result for f in futures]
return sum(results) / len(results)
五、實際應用建議
開發優先級
1. 基礎框架：先實現可靠的規則引擎
2. 數學核心：完善概率計算和決策邏輯
3. 數據驅動：添加機器學習和自適應能力
4. 性能優化：針對實時性要求進行優化
測試驗證
python
def test_ai_performance:
AI性能基準測試
test_cases = load_test_hands
correct_decisions = 0
for case in test_cases:
ai_decision = poker_ai.make_decision(case.game_state)
if ai_decision == case.optimal_decision:
correct_decisions += 1
accuracy = correct_decisions / len(test_cases)
print(f"AI決策準確率: {accuracy:.2%}")
這套算法體系結合了傳統的博弈論分析和現代的機器學習技術，能夠在復雜的撲克游戲中做出接近最優的決策。
