Update README.md
Browse files
README.md
CHANGED
@@ -30,8 +30,77 @@ TODO: Add your code
|
|
30 |
|
31 |
|
32 |
```python
|
33 |
-
|
34 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
35 |
|
36 |
-
...
|
37 |
```
|
|
|
30 |
|
31 |
|
32 |
```python
|
33 |
+
import gymnasium
|
34 |
+
|
35 |
+
from huggingface_sb3 import load_from_hub, package_to_hub
|
36 |
+
from huggingface_hub import notebook_login # To log to our Hugging Face account to be able to upload models to the Hub.
|
37 |
+
|
38 |
+
from stable_baselines3 import PPO
|
39 |
+
from stable_baselines3.common.env_util import make_vec_env
|
40 |
+
from stable_baselines3.common.evaluation import evaluate_policy
|
41 |
+
from stable_baselines3.common.monitor import Monitor
|
42 |
+
|
43 |
+
# Create the environment
|
44 |
+
env = make_vec_env('LunarLander-v2', n_envs=16)
|
45 |
+
|
46 |
+
model = PPO(
|
47 |
+
policy = 'MlpPolicy',
|
48 |
+
env = env,
|
49 |
+
n_steps = 1024,
|
50 |
+
batch_size = 64,
|
51 |
+
n_epochs = 4,
|
52 |
+
gamma = 0.999,
|
53 |
+
gae_lambda = 0.98,
|
54 |
+
ent_coef = 0.01,
|
55 |
+
verbose=1)
|
56 |
+
|
57 |
+
# Train it for 1,000,000 timesteps
|
58 |
+
model.learn(total_timesteps=1000000)
|
59 |
+
# Save the model
|
60 |
+
model_name = "ppo-LunarLander-v2"
|
61 |
+
model.save(model_name)
|
62 |
+
|
63 |
+
#@title
|
64 |
+
eval_env = Monitor(gym.make("LunarLander-v2"))
|
65 |
+
mean_reward, std_reward = evaluate_policy(model, eval_env, n_eval_episodes=10, deterministic=True)
|
66 |
+
print(f"mean_reward={mean_reward:.2f} +/- {std_reward}")
|
67 |
+
|
68 |
+
notebook_login()
|
69 |
+
!git config --global credential.helper store
|
70 |
+
|
71 |
+
import gymnasium as gym
|
72 |
+
|
73 |
+
from stable_baselines3 import PPO
|
74 |
+
from stable_baselines3.common.vec_env import DummyVecEnv
|
75 |
+
from stable_baselines3.common.env_util import make_vec_env
|
76 |
+
|
77 |
+
from huggingface_sb3 import package_to_hub
|
78 |
+
|
79 |
+
# PLACE the variables you've just defined two cells above
|
80 |
+
# Define the name of the environment
|
81 |
+
env_id = "LunarLander-v2"
|
82 |
+
|
83 |
+
# TODO: Define the model architecture we used
|
84 |
+
model_architecture = "PPO"
|
85 |
+
|
86 |
+
## Define a repo_id
|
87 |
+
## repo_id is the id of the model repository from the Hugging Face Hub (repo_id = {organization}/{repo_name} for instance ThomasSimonini/ppo-LunarLander-v2
|
88 |
+
## CHANGE WITH YOUR REPO ID
|
89 |
+
repo_id = "ThomasSimonini/ppo-LunarLander-v2" # Change with your repo id, you can't push with mine 😄
|
90 |
+
|
91 |
+
## Define the commit message
|
92 |
+
commit_message = "Upload PPO LunarLander-v2 trained agent"
|
93 |
+
|
94 |
+
# Create the evaluation env and set the render_mode="rgb_array"
|
95 |
+
eval_env = DummyVecEnv([lambda: gym.make(env_id, render_mode="rgb_array")])
|
96 |
+
|
97 |
+
# PLACE the package_to_hub function you've just filled here
|
98 |
+
package_to_hub(model=model, # Our trained model
|
99 |
+
model_name=model_name, # The name of our trained model
|
100 |
+
model_architecture=model_architecture, # The model architecture we used: in our case PPO
|
101 |
+
env_id=env_id, # Name of the environment
|
102 |
+
eval_env=eval_env, # Evaluation Environment
|
103 |
+
repo_id=repo_id, # id of the model repository from the Hugging Face Hub (repo_id = {organization}/{repo_name} for instance ThomasSimonini/ppo-LunarLander-v2
|
104 |
+
commit_message=commit_message)
|
105 |
|
|
|
106 |
```
|