
# **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).

## Training

```python
from stable_baselines3 import PPO
from stable_baselines3.common.env_util import make_vec_env

env = make_vec_env("LunarLander-v2", n_envs=16)
model = PPO('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)
model.learn(total_timesteps=10000000, progress_bar=True)
```


## Usage (with Stable-baselines3)


```python
from stable_baselines3 import PPO
from huggingface_sb3 import load_from_hub

repo_id = "zhuqi/PPO_LunarLander-v2_steps10M"  # The repo_id
filename = "PPO_LunarLander-v2_steps10000000.zip"  # The model filename.zip

# When the model was trained on Python 3.8 the pickle protocol is 5
# But Python 3.6, 3.7 use protocol 4
# In order to get compatibility we need to:
# 1. Install pickle5 (we done it at the beginning of the colab)
# 2. Create a custom empty object we pass as parameter to PPO.load()
custom_objects = {
    "learning_rate": 0.0,
    "lr_schedule": lambda _: 0.0,
    "clip_range": lambda _: 0.0,
}

checkpoint = load_from_hub(repo_id, filename)
model = PPO.load(checkpoint, custom_objects=custom_objects, print_system_info=True)
```

{"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": "284.96 +/- 22.41", "name": "mean_reward", "verified": false}]}]}]}
stable-baselines3 LunarLander-v2 deep-reinforcement-learning reinforcement-learning model-index region:us
TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us 


# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.

## Training




## Usage (with Stable-baselines3)




# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library. ## Training ## Usage (with Stable-baselines3)
TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us 
 # PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library. ## Training ## Usage (with Stable-baselines3)
TAGS
#stable-baselines3 #LunarLander-v2 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us 
# PPO Agent playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2
using the stable-baselines3 library.## Training## Usage (with Stable-baselines3)