# ThomasSimonini/ppo-SpaceInvadersNoFrameskip-v4

This is a pre-trained model of a PPO agent playing SpaceInvadersNoFrameskip using the [stable-baselines3](https://github.com/DLR-RM/stable-baselines3) library. It is taken from [RL-trained-agents](https://github.com/DLR-RM/rl-trained-agents)


### Usage (with Stable-baselines3)
Using this model becomes easy when you have stable-baselines3 and huggingface_sb3 installed:
```
pip install stable-baselines3
pip install huggingface_sb3
```

Then, you can use the model like this:

```python
import gym

from huggingface_sb3 import load_from_hub
from stable_baselines3 import PPO
from stable_baselines3.common.evaluation import evaluate_policy
from stable_baselines3.common.env_util import make_atari_env
from stable_baselines3.common.vec_env import VecFrameStack

# Retrieve the model from the hub
## repo_id =  id of the model repository from the Hugging Face Hub (repo_id = {organization}/{repo_name})
## filename = name of the model zip file from the repository
checkpoint = load_from_hub(repo_id="ThomasSimonini/ppo-SpaceInvadersNoFrameskip-v4", filename="ppo-SpaceInvadersNoFrameskip-v4.zip")
model = PPO.load(checkpoint)
```

### Evaluation Results
Mean_reward: 627.160 (162 eval episodes)


{"tags": ["deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"]}
stable-baselines3 deep-reinforcement-learning reinforcement-learning region:us
TAGS
#stable-baselines3 #deep-reinforcement-learning #reinforcement-learning #region-us 

# ThomasSimonini/ppo-SpaceInvadersNoFrameskip-v4

This is a pre-trained model of a PPO agent playing SpaceInvadersNoFrameskip using the stable-baselines3 library. It is taken from RL-trained-agents


### Usage (with Stable-baselines3)
Using this model becomes easy when you have stable-baselines3 and huggingface_sb3 installed:


Then, you can use the model like this:



### Evaluation Results
Mean_reward: 627.160 (162 eval episodes)


# ThomasSimonini/ppo-SpaceInvadersNoFrameskip-v4

This is a pre-trained model of a PPO agent playing SpaceInvadersNoFrameskip using the stable-baselines3 library. It is taken from RL-trained-agents ### Usage (with Stable-baselines3)
Using this model becomes easy when you have stable-baselines3 and huggingface_sb3 installed:


Then, you can use the model like this: ### Evaluation Results
Mean_reward: 627.160 (162 eval episodes)
TAGS
#stable-baselines3 #deep-reinforcement-learning #reinforcement-learning #region-us 
 # ThomasSimonini/ppo-SpaceInvadersNoFrameskip-v4

This is a pre-trained model of a PPO agent playing SpaceInvadersNoFrameskip using the stable-baselines3 library. It is taken from RL-trained-agents ### Usage (with Stable-baselines3)
Using this model becomes easy when you have stable-baselines3 and huggingface_sb3 installed:


Then, you can use the model like this: ### Evaluation Results
Mean_reward: 627.160 (162 eval episodes)
TAGS
#stable-baselines3 #deep-reinforcement-learning #reinforcement-learning #region-us 
# ThomasSimonini/ppo-SpaceInvadersNoFrameskip-v4

This is a pre-trained model of a PPO agent playing SpaceInvadersNoFrameskip using the stable-baselines3 library. It is taken from RL-trained-agents### Usage (with Stable-baselines3)
Using this model becomes easy when you have stable-baselines3 and huggingface_sb3 installed:


Then, you can use the model like this:### Evaluation Results
Mean_reward: 627.160 (162 eval episodes)