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---
library_name: stable-baselines3
tags:
- Pendulum-v1
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: ARS
results:
- metrics:
- type: mean_reward
value: -282.08 +/- 194.51
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pendulum-v1
type: Pendulum-v1
---
# **ARS** Agent playing **Pendulum-v1**
This is a trained model of a **ARS** 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 ars --env Pendulum-v1 -orga sb3 -f logs/
python enjoy.py --algo ars --env Pendulum-v1 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo ars --env Pendulum-v1 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo ars --env Pendulum-v1 -f logs/ -orga sb3
```
## Hyperparameters
```python
OrderedDict([('delta_std', 0.1),
('learning_rate', 0.018),
('n_delta', 4),
('n_envs', 8),
('n_timesteps', 2000000.0),
('n_top', 1),
('normalize', 'dict(norm_obs=True, norm_reward=False)'),
('policy', 'MlpPolicy'),
('policy_kwargs', 'dict(net_arch=[16])'),
('zero_policy', False),
('normalize_kwargs', {'norm_obs': True, 'norm_reward': False})])
```
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