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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: 330.99 +/- 20.23
name: mean_reward
verified: true
---
# **PPO** Agent playing **LunarLander-v2**
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)
```python
from stable_baselines3 import PPO
from stable_baselines3.common.env_util import make_vec_env
from stable_baselines3.common.evaluation import evaluate_policy
from huggingface_sb3 import load_from_hub
# Download the model checkpoint
model_checkpoint = load_from_hub("DeathReaper0965/ppo-mlp-LunarLander-v2", "ppo-mlp-LunarLander-v2.zip")
# Create a vectorized environment
env = make_vec_env("LunarLander-v2", n_envs=1)
# Load the model
model = PPO.load(model_checkpoint, env=env)
# Evaluate
print("Evaluating model")
mean_reward, std_reward = evaluate_policy(
model,
env,
n_eval_episodes=30,
deterministic=True,
)
print(f"Mean reward = {mean_reward:.2f} +/- {std_reward}")
# Start a new episode
obs = env.reset()
try:
while True:
action, state = model.predict(obs, deterministic=True)
obs, reward, done, info = env.step(action)
env.render()
except KeyboardInterrupt:
pass
```
## Conclusion
The above steps ensure that the Agent Model gets downloaded successfully. <br>
You may need to download the required libraries and packages specific to your operating system to resume training from the supplied checkpoint and run it for more steps.
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