File size: 5,398 Bytes
064e771 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 | from __future__ import annotations
import datetime as dt
import json
import shutil
from pathlib import Path
import gymnasium as gym
import imageio
import numpy as np
from huggingface_hub import HfApi
from huggingface_hub.errors import HfHubHTTPError
from huggingface_hub.repocard import metadata_eval_result, metadata_save
from stable_baselines3 import PPO
from stable_baselines3.common.env_util import make_vec_env
from stable_baselines3.common.evaluation import evaluate_policy
from stable_baselines3.common.monitor import Monitor
USERNAME = "Sami94"
STUDENT_NAME = "Sami Chellia"
ENV_ID = "LunarLander-v2"
MODEL_NAME = "ppo-LunarLander-v2"
REPO_ID = f"{USERNAME}/{MODEL_NAME}"
OUTPUT_DIR = Path("artifacts/unit1") / MODEL_NAME
def evaluate(model: PPO, n_eval_episodes: int = 10) -> tuple[float, float]:
eval_env = Monitor(gym.make(ENV_ID, render_mode="rgb_array"))
mean_reward, std_reward = evaluate_policy(
model,
eval_env,
n_eval_episodes=n_eval_episodes,
deterministic=True,
)
eval_env.close()
return float(mean_reward), float(std_reward)
def record_video(model: PPO, out_path: Path, max_steps: int = 1000) -> None:
env = gym.make(ENV_ID, render_mode="rgb_array")
obs, _ = env.reset(seed=42)
frames = [env.render()]
for _ in range(max_steps):
action, _ = model.predict(obs, deterministic=True)
obs, _, terminated, truncated, _ = env.step(action)
frames.append(env.render())
if terminated or truncated:
break
env.close()
imageio.mimsave(out_path, [np.asarray(frame) for frame in frames], fps=30)
def save_artifacts(model: PPO, mean_reward: float, std_reward: float, timesteps: int) -> None:
if OUTPUT_DIR.exists():
shutil.rmtree(OUTPUT_DIR)
OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
model.save(OUTPUT_DIR / MODEL_NAME)
record_video(model, OUTPUT_DIR / "replay.mp4")
results = {
"env_id": ENV_ID,
"mean_reward": mean_reward,
"std_reward": std_reward,
"n_eval_episodes": 10,
"total_timesteps": timesteps,
"eval_datetime": dt.datetime.now().isoformat(),
"student": STUDENT_NAME,
"hf_username": USERNAME,
}
(OUTPUT_DIR / "results.json").write_text(json.dumps(results, indent=2), encoding="utf-8")
metadata = {
"tags": [
ENV_ID,
"ppo",
"stable-baselines3",
"reinforcement-learning",
"huggingface-deep-rl-course",
]
}
eval_metadata = metadata_eval_result(
model_pretty_name=MODEL_NAME,
task_pretty_name="reinforcement-learning",
task_id="reinforcement-learning",
metrics_pretty_name="mean_reward",
metrics_id="mean_reward",
metrics_value=f"{mean_reward:.2f} +/- {std_reward:.2f}",
dataset_pretty_name=ENV_ID,
dataset_id=ENV_ID,
)
metadata = {**metadata, **eval_metadata}
readme_path = OUTPUT_DIR / "README.md"
readme_path.write_text(
f"""# PPO Agent for {ENV_ID}
Student: {STUDENT_NAME}
Hugging Face username: {USERNAME}
This repository contains a PPO agent trained for the Hugging Face Deep RL course.
Mean reward: {mean_reward:.2f} +/- {std_reward:.2f}
Total timesteps: {timesteps}
```python
from huggingface_hub import hf_hub_download
from stable_baselines3 import PPO
model_path = hf_hub_download(repo_id="{REPO_ID}", filename="{MODEL_NAME}.zip")
model = PPO.load(model_path)
```
""",
encoding="utf-8",
)
metadata_save(readme_path, metadata)
def push_artifacts() -> str:
api = HfApi()
try:
repo_url = api.create_repo(repo_id=REPO_ID, repo_type="model", exist_ok=True)
api.upload_folder(repo_id=REPO_ID, repo_type="model", folder_path=OUTPUT_DIR, path_in_repo=".")
return str(repo_url)
except HfHubHTTPError as exc:
print(f"Hub push failed for {REPO_ID}: {exc}")
print(f"Local artifacts saved in {OUTPUT_DIR.resolve()}")
return f"LOCAL_ONLY:{OUTPUT_DIR.resolve()}"
def main() -> None:
env = make_vec_env(ENV_ID, n_envs=16)
model = PPO(
policy="MlpPolicy",
env=env,
n_steps=1024,
batch_size=64,
n_epochs=4,
gamma=0.999,
gae_lambda=0.98,
ent_coef=0.01,
verbose=1,
)
total_timesteps = 0
best: tuple[float, float] | None = None
for chunk in [200_000, 200_000, 200_000, 200_000, 200_000]:
model.learn(total_timesteps=chunk, reset_num_timesteps=False)
total_timesteps += chunk
mean_reward, std_reward = evaluate(model)
best = (mean_reward, std_reward)
print(f"Evaluation after {total_timesteps} timesteps: {mean_reward:.2f} +/- {std_reward:.2f}")
save_artifacts(model, mean_reward, std_reward, total_timesteps)
if mean_reward >= 200:
print("Certification threshold reached for Unit 1.")
break
env.close()
if best is None:
raise RuntimeError("Training finished without evaluation.")
url = push_artifacts()
(OUTPUT_DIR / "pushed_repo.json").write_text(
json.dumps({"repo_id": REPO_ID, "url": url}, indent=2),
encoding="utf-8",
)
print(json.dumps({"repo_id": REPO_ID, "url": url, "mean_reward": best[0], "std_reward": best[1]}, indent=2))
if __name__ == "__main__":
main()
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