feat: CSV加player列+#RESULT汇总 — 训练中直接看胜率进化

PdkFriendServer/Logic/PdkGameMain.cs

@@ -499,7 +499,7 @@ namespace PdkFriendServer.Logic
                                     float s = GamePack.Info.Score[i];
                                     rewards[i] = s > 0 ? 1f : s < 0 ? -1f : 0f;
                                 }
-                                TrainingCollector.FlushGame(_flushGameCounter++, rewards);
+                                TrainingCollector.FlushGame(_flushGameCounter++, rewards, GamePack.Info.OverPos, BotTypes);
                             }
 
                             if (DeskGameDo != null)

PdkFriendServer/Logic/TrainingCollector.cs

@@ -39,19 +39,21 @@ namespace PdkFriendServer.Logic
         /// 游戏结束，输出本轮训练数据到文件。
         /// rewards: 每个玩家的奖励 (1=赢, 0=第二, -1=第三)
         /// </summary>
-        public static void FlushGame(int gameId, float[] rewards)
+        public static void FlushGame(int gameId, float[] rewards, int winnerPos, string[] botTypes)
         {
             if (!Enabled || _records.Count == 0) return;
             var path = $"training_data/game_{gameId:D5}.csv";
             System.IO.Directory.CreateDirectory("training_data");
             using var w = new System.IO.StreamWriter(path);
-            w.WriteLine("state,action,reward");
+            w.WriteLine("state,action,reward,player");
             foreach (var r in _records)
             {
                 float reward = r.Player >= 0 && r.Player < rewards.Length ? rewards[r.Player] : 0;
                 var stateStr = string.Join(",", r.State.Select(f => f.ToString("F6")));
-                w.WriteLine($"\"{stateStr}\",{r.Action},{reward:F3}");
+                w.WriteLine($"\"{stateStr}\",{r.Action},{reward:F3},{r.Player}");
             }
+            // 游戏结果摘要 — 方便不中断训练即可评估模型进化
+            w.WriteLine($"#RESULT,winner={winnerPos},bots={string.Join(",", botTypes ?? new[]{"ISMCTS","ISMCTS","ISMCTS"})}");
             _records.Clear();
         }
 

scripts/scan_wins.py

@@ -0,0 +1,67 @@
+#!/usr/bin/env python3
+"""扫描 training_data 目录下所有 CSV，按 bot 类型统计胜率"""
+import sys, os, glob, re
+from collections import Counter, defaultdict
+
+def scan(csv_dir='training_data'):
+    csvs = sorted(glob.glob(os.path.join(csv_dir, 'game_*.csv')))
+    if not csvs:
+        print("No CSV files found")
+        return
+    
+    total = 0
+    per_bot = defaultdict(lambda: {'wins': 0, 'games': 0})
+    per_game = []  # (game_id, winner_pos, bots_str)
+    
+    for fp in csvs:
+        with open(fp) as f:
+            for line in f:
+                if line.startswith('#RESULT'):
+                    parts = line.strip().split(',')
+                    winner = int(parts[1].split('=')[1])
+                    bots = parts[2].split('=')[1].split(',')
+                    game_id = os.path.splitext(os.path.basename(fp))[0]
+                    per_game.append((game_id, winner, line.strip()))
+                    total += 1
+                    
+                    # winner_pos is 1-indexed, bots[winner-1]
+                    for i, bt in enumerate(bots):
+                        per_bot[bt]['games'] += 1
+                    winner_bt = bots[winner - 1] if 0 < winner <= len(bots) else '?'
+                    per_bot[winner_bt]['wins'] += 1
+                    break
+    
+    if total == 0:
+        print("No #RESULT lines found. Run with updated hjha-console.")
+        return
+    
+    print(f"{total} games scanned")
+    print(f"{'Bot':<12} {'Wins':>6} {'Rate':>8} {'Games':>6}")
+    print("-" * 35)
+    for bt in sorted(per_bot.keys()):
+        d = per_bot[bt]
+        rate = d['wins'] / d['games'] * 100 if d['games'] > 0 else 0
+        print(f"{bt:<12} {d['wins']:>6} {rate:>7.1f}% {d['games']:>6}")
+    
+    # 也统计按 bot 组合
+    combo_stats = defaultdict(lambda: {'total': 0, 'winners': Counter()})
+    for gid, winner, line in per_game:
+        m = re.search(r'bots=([^,]+(?:,[^,]+)*)', line)
+        if m:
+            key = m.group(1)
+            combo_stats[key]['total'] += 1
+            combo_stats[key]['winners'][winner] += 1
+    
+    if len(combo_stats) > 0:
+        print(f"\nBy bot composition:")
+        for combo in sorted(combo_stats.keys()):
+            d = combo_stats[combo]
+            bots = combo.split(',')
+            print(f"  [{combo}] — {d['total']} games:")
+            for pos, count in sorted(d['winners'].items()):
+                bt = bots[pos-1] if 0 < pos <= len(bots) else '?'
+                print(f"    pos{pos}({bt}): {count}/{d['total']} ({count/d['total']*100:.0f}%)")
+
+if __name__ == '__main__':
+    d = sys.argv[1] if len(sys.argv) > 1 else 'training_data'
+    scan(d)