---
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: 242.40 +/- 15.91
      name: mean_reward
      verified: false
---

# **PPO** Agent playing **LunarLander-v2**
This model is trained using **PPO [proximal policy optimization algorithm invented by OpenAI]** The RL-based agent playing to land correctly on the moon using **LunarLander** environment as simulator. 

## Usage (with Stable-baselines3)
TODO: Add your code


```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub

repo_id = "innocent-charles/RL-ppo-LunarLander-v2"
filename = "RL-ppo-LunarLander-v2.zip"

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)

...
```