
# **PPO** Agent playing **Pendulum-v1**
This is a trained model of a **PPO** agent playing **Pendulum-v1**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).

The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.

## Usage (with SB3 RL Zoo)

RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib

```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo ppo --env Pendulum-v1 -orga sb3 -f logs/
python enjoy.py --algo ppo --env Pendulum-v1  -f logs/
```

## Training (with the RL Zoo)
```
python train.py --algo ppo --env Pendulum-v1 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo ppo --env Pendulum-v1 -f logs/ -orga sb3
```

```python
from stable_baselines3 import PPO
from stable_baselines3.common.env_util import make_vec_env

# Create the environment
env_id = "Pendulum-v1"
env = make_vec_env(env_id, n_envs=1)

# Instantiate the agent
model = PPO(
    "MlpPolicy",
    env,
    gamma=0.98,
    # Using https://proceedings.mlr.press/v164/raffin22a.html
    use_sde=True,
    sde_sample_freq=4,
    learning_rate=1e-3,
    verbose=1,
)

# Train the agent
model.learn(total_timesteps=int(1e5))
```

## Hyperparameters
```python
OrderedDict([('clip_range', 0.2),
             ('ent_coef', 0.0),
             ('gae_lambda', 0.95),
             ('gamma', 0.9),
             ('learning_rate', 0.001),
             ('n_envs', 4),
             ('n_epochs', 10),
             ('n_steps', 1024),
             ('n_timesteps', 100000.0),
             ('policy', 'MlpPolicy'),
             ('sde_sample_freq', 4),
             ('use_sde', True),
             ('normalize', False)])
```

{"library_name": "stable-baselines3", "tags": ["Pendulum-v1", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Pendulum-v1", "type": "Pendulum-v1"}, "metrics": [{"type": "mean_reward", "value": "-230.42 +/- 142.54", "name": "mean_reward"}]}]}]}
stable-baselines3 Pendulum-v1 deep-reinforcement-learning reinforcement-learning model-index region:us
TAGS
#stable-baselines3 #Pendulum-v1 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us 


# PPO Agent playing Pendulum-v1
This is a trained model of a PPO agent playing Pendulum-v1
using the stable-baselines3 library
and the RL Zoo.

The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.

## Usage (with SB3 RL Zoo)

RL Zoo: URL
SB3: URL
SB3 Contrib: URL



## Training (with the RL Zoo)




## Hyperparameters


# PPO Agent playing Pendulum-v1
This is a trained model of a PPO agent playing Pendulum-v1
using the stable-baselines3 library
and the RL Zoo.

The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo)

RL Zoo: URL
SB3: URL
SB3 Contrib: URL ## Training (with the RL Zoo) ## Hyperparameters
TAGS
#stable-baselines3 #Pendulum-v1 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us 
 # PPO Agent playing Pendulum-v1
This is a trained model of a PPO agent playing Pendulum-v1
using the stable-baselines3 library
and the RL Zoo.

The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo)

RL Zoo: URL
SB3: URL
SB3 Contrib: URL ## Training (with the RL Zoo) ## Hyperparameters
passage: TAGS
#stable-baselines3 #Pendulum-v1 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us 
# PPO Agent playing Pendulum-v1
This is a trained model of a PPO agent playing Pendulum-v1
using the stable-baselines3 library
and the RL Zoo.

The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.## Usage (with SB3 RL Zoo)

RL Zoo: URL
SB3: URL
SB3 Contrib: URL## Training (with the RL Zoo)## Hyperparameters