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---
library_name: stable-baselines3
tags:
- HalfCheetah-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: ARS
results:
- metrics:
- type: mean_reward
value: 4046.14 +/- 2253.39
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: HalfCheetah-v3
type: HalfCheetah-v3
---
# **ARS** Agent playing **HalfCheetah-v3**
This is a trained model of a **ARS** agent playing **HalfCheetah-v3**
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 utils.load_from_hub --algo ars --env HalfCheetah-v3 -orga sb3 -f logs/
python enjoy.py --algo ars --env HalfCheetah-v3 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo ars --env HalfCheetah-v3 -f logs/
# Upload the model and generate video (when possible)
python -m utils.push_to_hub --algo ars --env HalfCheetah-v3 -f logs/ -orga sb3
```
## Hyperparameters
```python
OrderedDict([('alive_bonus_offset', 0),
('delta_std', 0.03),
('learning_rate', 0.02),
('n_delta', 32),
('n_envs', 16),
('n_timesteps', 12500000.0),
('n_top', 4),
('normalize', 'dict(norm_obs=True, norm_reward=False)'),
('policy', 'LinearPolicy'),
('normalize_kwargs', {'norm_obs': True, 'norm_reward': False})])
```
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