File size: 3,270 Bytes
e8df7ab 026cee9 e8df7ab d6e7bb2 e8df7ab d6e7bb2 e8df7ab d6e7bb2 e8df7ab d6e7bb2 |
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 |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 298.48 +/- 12.14
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Re-train model (with Stable-baselines3)
TODO: Add your code
```python
# Load a saved LunarLander model from the Hub and retrain
import gym
from huggingface_sb3 import load_from_hub, package_to_hub, push_to_hub
from huggingface_hub import notebook_login # To log to our Hugging Face account to be able to upload models to the Hub.
from stable_baselines3 import PPO
from stable_baselines3.common.evaluation import evaluate_policy
from stable_baselines3.common.env_util import make_vec_env
from stable_baselines3.common.vec_env import DummyVecEnv
repo_id = "thien1892/LunarLander-v2-ppo-v5"
filename = "ppo-LunarLander-v2.zip" # The model filename.zip
checkpoint = load_from_hub(repo_id, filename)
myenv = make_vec_env('LunarLander-v2', n_envs=16)
custom_objects = {
"learning_rate": 1e-5,
"clip_range": lambda _: 0.15,
}
model = PPO.load(checkpoint, reset_num_timesteps=True, print_system_info=True,custom_objects = custom_objects, env = myenv)
# Train it for 1,000,000 timesteps
model.learn(total_timesteps=1000000)
# Save the model
model_name = "ppo-LunarLander-v2-5m"
model.save(model_name)
# Evaluate
eval_env = gym.make("LunarLander-v2")
mean_reward, std_reward = evaluate_policy(model, eval_env, n_eval_episodes=10, deterministic=True)
print(f"mean_reward={mean_reward:.2f} +/- {std_reward}")
```
## Pust to HF hub
```python
notebook_login()
!git config --global credential.helper store
```
```
## 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
repo_id = "thien1892/LunarLander-v2-ppo-5m"
# TODO: Define the name of the environment
env_id = "LunarLander-v2"
# Create the evaluation env
eval_env = DummyVecEnv([lambda: gym.make(env_id)])
# TODO: Define the model architecture we used
model_architecture = "PPO"
## TODO: Define the commit message
commit_message = "Upload PPO LunarLander-v2 trained agent"
# method save, evaluate, generate a model card and record a replay video of your agent before pushing the repo to the hub
package_to_hub(model=model, # Our trained model
model_name=model_name, # The name of our trained model
model_architecture=model_architecture, # The model architecture we used: in our case PPO
env_id=env_id, # Name of the environment
eval_env=eval_env, # Evaluation Environment
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
commit_message=commit_message)
``` |