File size: 1,920 Bytes
faa6c46 587d0e1 faa6c46 587d0e1 faa6c46 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 |
---
library_name: stable-baselines3
tags:
- Walker2d-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: ARS
results:
- metrics:
- type: mean_reward
value: 2958.36 +/- 195.43
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Walker2d-v3
type: Walker2d-v3
---
# **ARS** Agent playing **Walker2d-v3**
This is a trained model of a **ARS** agent playing **Walker2d-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 rl_zoo3.load_from_hub --algo ars --env Walker2d-v3 -orga sb3 -f logs/
python enjoy.py --algo ars --env Walker2d-v3 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo ars --env Walker2d-v3 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo ars --env Walker2d-v3 -f logs/ -orga sb3
```
## Hyperparameters
```python
OrderedDict([('alive_bonus_offset', -1),
('delta_std', 0.025),
('learning_rate', 0.03),
('n_delta', 40),
('n_envs', 16),
('n_timesteps', 75000000.0),
('n_top', 30),
('normalize', 'dict(norm_obs=True, norm_reward=False)'),
('policy', 'LinearPolicy'),
('normalize_kwargs', {'norm_obs': True, 'norm_reward': False})])
```
|