
# **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.

{"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}]}]}]}
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
A trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.

## Usage (with Stable-baselines3)



## 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.

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