BC算法实现

Algorithm/bc.py

@@ -0,0 +1,52 @@
+"""
+Behavior Cloning (BC) 算法：仅包含损失与单 epoch 训练/评估逻辑。
+数据加载、环境评估、日志与保存由训练脚本 (train_bc.py) 负责。
+"""
+import torch
+
+
+def bc_loss(policy, states, actions):
+    """
+    BC 损失：负对数似然 -E[log pi(a|s)]。
+    states: (B, state_dim), actions: (B, action_dim), 均在 policy 所在 device 上。
+    """
+    log_pi = policy.evaluate_log_pi(states, actions)
+    return -log_pi.mean()
+
+
+def train_bc_epoch(policy, train_loader, optimizer, device):
+    """
+    训练一个 epoch，返回平均 train loss。
+    policy 与 optimizer 由调用方管理，本函数只做前向、损失、反向与 step。
+    """
+    policy.train()
+    total_loss = 0.0
+    n_batches = 0
+    for states, actions in train_loader:
+        states = states.to(device)
+        actions = actions.to(device)
+        loss = bc_loss(policy, states, actions)
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()
+        total_loss += loss.item()
+        n_batches += 1
+    return total_loss / n_batches if n_batches else 0.0
+
+
+def eval_bc_epoch(policy, val_loader, device):
+    """
+    在验证集上评估一个 epoch，返回平均 val loss（无梯度）。
+    """
+    policy.eval()
+    total_loss = 0.0
+    n_batches = 0
+    with torch.no_grad():
+        for states, actions in val_loader:
+            states = states.to(device)
+            actions = actions.to(device)
+            log_pi = policy.evaluate_log_pi(states, actions)
+            loss = -log_pi.mean().item()
+            total_loss += loss
+            n_batches += 1
+    return total_loss / n_batches if n_batches else 0.0