Upload Unit_3_upload.py with huggingface_hub

Unit_3_upload.py

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+# ============================================================
+# 评估模型并上传到 Hugging Face（修复版）
+# ============================================================
+
+import gymnasium as gym
+import ale_py
+import numpy as np
+import os
+import shutil
+from stable_baselines3 import DQN
+from stable_baselines3.common.env_util import make_atari_env
+from stable_baselines3.common.vec_env import VecFrameStack
+from huggingface_hub import HfApi, create_repo
+
+# ============================================================
+# 配置参数（修改这里）
+# ============================================================
+USERNAME = "ImaghT"
+MODEL_NAME = "dqn-SpaceInvadersNoFrameskip-v4"
+MODEL_FILE = "dqn-SpaceInvaders.zip"
+ENV_ID = "ALE/SpaceInvaders-v5"
+N_EVAL_EPISODES = 10
+
+repo_id = f"{USERNAME}/{MODEL_NAME}"
+
+# ============================================================
+# 1. 注册 ALE 环境
+# ============================================================
+print("Registering ALE environments...")
+gym.register_envs(ale_py)
+print("✅ Environments registered\n")
+
+# ============================================================
+# 2. 加载训练好的模型
+# ============================================================
+print("Loading trained model...")
+if not os.path.exists(MODEL_FILE):
+    print(f"❌ Error: Model file '{MODEL_FILE}' not found!")
+    exit(1)
+
+model = DQN.load(MODEL_FILE)
+print(f"✅ Model loaded from {MODEL_FILE}\n")
+
+# ============================================================
+# 3. 创建评估环境
+# ============================================================
+print("Creating evaluation environment...")
+eval_env = make_atari_env(ENV_ID, n_envs=1, seed=42)
+eval_env = VecFrameStack(eval_env, n_stack=4)
+print("✅ Evaluation environment ready\n")
+
+# ============================================================
+# 4. 运行评估（正确方法）
+# ============================================================
+print("="*60)
+print(f"Starting Evaluation ({N_EVAL_EPISODES} episodes)...")
+print("="*60)
+
+episode_rewards = []
+episode_lengths = []
+
+obs = eval_env.reset()
+current_episode = 0
+
+# 持续运行直到收集到足够的 episode
+while len(episode_rewards) < N_EVAL_EPISODES:
+    action, _states = model.predict(obs, deterministic=True)
+    obs, reward, done, info = eval_env.step(action)
+    
+    # 🔥 关键修复：在 VecEnv 中，episode 结束时真实 reward 在 info 中
+    if done[0]:
+        # info 是一个列表，info[0] 包含第一个环境的信息
+        if 'episode' in info[0]:
+            ep_reward = info[0]['episode']['r']
+            ep_length = info[0]['episode']['l']
+            episode_rewards.append(ep_reward)
+            episode_lengths.append(ep_length)
+            print(f"Episode {len(episode_rewards)}/{N_EVAL_EPISODES}: "
+                  f"Reward = {ep_reward:.2f}, Length = {ep_length}")
+
+# ============================================================
+# 5. 计算统计数据
+# ============================================================
+mean_reward = np.mean(episode_rewards)
+std_reward = np.std(episode_rewards)
+min_reward = np.min(episode_rewards)
+max_reward = np.max(episode_rewards)
+mean_length = np.mean(episode_lengths)
+score = mean_reward - std_reward
+
+print("\n" + "="*60)
+print("Evaluation Results:")
+print(f"  Mean Reward: {mean_reward:.2f}")
+print(f"  Std Reward:  {std_reward:.2f}")
+print(f"  Min Reward:  {min_reward:.2f}")
+print(f"  Max Reward:  {max_reward:.2f}")
+print(f"  Mean Length: {mean_length:.2f}")
+print(f"  Score (mean - std): {score:.2f}")
+print(f"  Baseline Required: 200.0")
+if score >= 200:
+    print(f"  Status: ✅ PASSED")
+else:
+    print(f"  Status: ❌ NOT PASSED (need {200 - score:.2f} more points)")
+print("="*60 + "\n")
+
+# ============================================================
+# 6. 创建 README.md
+# ============================================================
+readme_content = f"""---
+library_name: stable-baselines3
+tags:
+- SpaceInvadersNoFrameskip-v4
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: DQN
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: SpaceInvadersNoFrameskip-v4
+      type: SpaceInvadersNoFrameskip-v4
+    metrics:
+    - type: mean_reward
+      value: {mean_reward:.2f} +/- {std_reward:.2f}
+      name: mean_reward
+      verified: false
+---
+
+# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
+
+This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
+using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
+and the [Deep Reinforcement Learning Course](https://huggingface.co/deep-rl-course/unit3).
+
+## Evaluation Results
+
+| Metric | Value |
+|--------|-------|
+| Mean Reward | {mean_reward:.2f} |
+| Std Reward | {std_reward:.2f} |
+| Min Reward | {min_reward:.2f} |
+| Max Reward | {max_reward:.2f} |
+| Mean Episode Length | {mean_length:.2f} |
+| Score (mean - std) | {score:.2f} |
+| Evaluation Episodes | {N_EVAL_EPISODES} |
+
+## Usage
+
+```python
+from stable_baselines3 import DQN
+from stable_baselines3.common.env_util import make_atari_env
+from stable_baselines3.common.vec_env import VecFrameStack
+import gymnasium as gym
+import ale_py
+
+gym.register_envs(ale_py)
+
+env = make_atari_env("ALE/SpaceInvaders-v5", n_envs=1, seed=0)
+env = VecFrameStack(env, n_stack=4)
+
+model = DQN.load("dqn-SpaceInvaders")
+
+obs = env.reset()
+for i in range(1000):
+    action, _states = model.predict(obs, deterministic=True)
+    obs, reward, done, info = env.step(action)
+    if done:
+        obs = env.reset()
+
+```
+## Training Configuration
+
+- **Algorithm**: DQN (Deep Q-Network)
+- **Policy**: CnnPolicy
+- **Total Timesteps**: 10,000,000
+- **Learning Rate**: 1e-4
+- **Buffer Size**: 200,000
+- **Batch Size**: 32
+- **Device**: CUDA
+"""
+
+# ============================================================
+# 7. 准备上传文件
+# ============================================================
+print("Preparing files for upload...")
+upload_folder = "./upload_temp"
+os.makedirs(upload_folder, exist_ok=True)
+
+readme_path = os.path.join(upload_folder, "README.md")
+with open(readme_path, "w", encoding="utf-8") as f:
+    f.write(readme_content)
+print(f"✅ Created README.md")
+
+model_dest = os.path.join(upload_folder, MODEL_FILE)
+shutil.copy(MODEL_FILE, model_dest)
+print(f"✅ Copied {MODEL_FILE}\n")
+
+# ============================================================
+# 8. 上传到 Hugging Face
+# ============================================================
+print(f"Uploading to {repo_id}...")
+
+api = HfApi()
+
+try:
+    create_repo(repo_id, repo_type="model", exist_ok=True)
+    print(f"✅ Repository created/verified")
+except Exception as e:
+    print(f"⚠️ Repository warning: {e}")
+
+try:
+    api.upload_folder(
+        folder_path=upload_folder,
+        repo_id=repo_id,
+        repo_type="model",
+        commit_message=f"DQN SpaceInvaders - Mean: {mean_reward:.2f}, Std: {std_reward:.2f}"
+    )
+    print(f"\n{'='*60}")
+    print("✅ Upload Successful!")
+    print(f"{'='*60}")
+    print(f"🔗 Model Page: https://huggingface.co/{repo_id}")
+    print(f"🏆 Check Progress: https://huggingface.co/spaces/ThomasSimonini/Check-my-progress-Deep-RL-Course")
+    print(f"{'='*60}\n")
+except Exception as e:
+    print(f"\n❌ Upload failed: {e}\n")
+finally:
+    shutil.rmtree(upload_folder)
+    print("🧹 Cleaned up temporary files")
+
+print("✨ Done!")