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---
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: 294.83 +/- 15.86
name: mean_reward
verified: false
license: mit
---
# **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).
## Usage (with Stable-baselines3)
TODO: Add to your code
```python
import gymnasium as gym
from stable_baselines3 import PPO
from huggingface_sb3 import load_from_hub
checkpoint_model = load_from_hub(
repo_id="Creador270/ppo-LunarLander-v2_hello_RL",
filename="ppo-LunarLander-v2_hello_RL_2.zip",
model = PPO.load(checkpoint_model) #The model you will be using
env = gym.make("LunarLander-v2")
observation, info = env.reset()
#It must be deterministic because, in the action space, we can only choose between which motor must be activated
action, _states = model.predict(observation, deterministic=True)
)
``` |