feat: improve V-training loop — weight decay, cosine LR, early stop

- Adam weight_decay=1e-5 to prevent overfitting
- CosineAnnealingLR 0.001→1e-5 over 50 epochs
- Early stopping after 10 epochs without improvement
- Epochs: 20→50 max (with early stop typically fewer)

scripts/training_orchestrator.py

@@ -29,7 +29,7 @@ def run(cmd, cwd=HJHA_DIR, to=86400):
         return False, str(e)
 
 def selfplay(games):
-    log(f"── 自对弈 {games}局 ──")
+    log(f"── 自対弈 {games}局 ──")
     td = os.path.join(HJHA_DIR, 'training_data')
     if os.path.exists(td): shutil.rmtree(td)
     os.makedirs(td)
@@ -80,12 +80,15 @@ def train_v():
     log(f"  {len(states)} samples from {len(glob.glob(f'{csv_dir}/game_*.csv'))} games")
     
     model = PdkNet()
-    opt = torch.optim.Adam(model.parameters(), lr=0.001)
+    opt = torch.optim.Adam(model.parameters(), lr=0.001, weight_decay=1e-5)
+    scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(opt, T_max=50, eta_min=1e-5)
     loss_fn = torch.nn.MSELoss()
     n = len(states)
     t0 = datetime.now()
+    best_loss = float('inf')
+    no_improve = 0
     
-    for ep in range(20):
+    for ep in range(50):
         perm = np.random.permutation(n)
         losses = []
         for i in range(0, n, 128):
@@ -96,12 +99,23 @@ def train_v():
             loss = loss_fn(pred, y)
             opt.zero_grad(); loss.backward(); opt.step()
             losses.append(loss.item())
-        if ep % 5 == 0:
+        scheduler.step()
+        avg_loss = np.mean(losses)
+        if avg_loss < best_loss * 0.999:
+            best_loss = avg_loss
+            no_improve = 0
+        else:
+            no_improve += 1
+        if ep % 5 == 0 or ep == 49:
             dt = (datetime.now() - t0).total_seconds()
-            log(f"  ep{ep+1}/20 loss={np.mean(losses):.4f} ({dt:.0f}s)")
+            lr = scheduler.get_last_lr()[0]
+            log(f"  ep{ep+1}/50 loss={avg_loss:.4f} lr={lr:.6f} ({dt:.0f}s)")
+        if no_improve >= 10:
+            log(f"  early stop ep{ep+1}, plateau 10 epochs")
+            break
     
     elapsed = (datetime.now() - t0).total_seconds()
-    log(f"  done {elapsed:.0f}s, final loss={np.mean(losses):.4f}")
+    log(f"  done {elapsed:.0f}s, final loss={avg_loss:.4f}")
     
     # save pt
     mp = os.path.join(TRAINER_DIR, 'data/model.pt')
@@ -155,7 +169,7 @@ def main():
         
         csv = selfplay(games)
         if csv == 0:
-            log("自对弈失败! 跳过")
+            log("自対弈失败! 跳过")
             continue
         total += games
         
@@ -166,4 +180,4 @@ def main():
         log(f"累计: {rn}轮, {total}局")
 
 if __name__ == '__main__':
-    main()
+    main()