|
--- |
|
library_name: stable-baselines3 |
|
tags: |
|
- Hopper-v3 |
|
- deep-reinforcement-learning |
|
- reinforcement-learning |
|
- stable-baselines3 |
|
model-index: |
|
- name: TD3 |
|
results: |
|
- metrics: |
|
- type: mean_reward |
|
value: 3604.63 +/- 4.84 |
|
name: mean_reward |
|
task: |
|
type: reinforcement-learning |
|
name: reinforcement-learning |
|
dataset: |
|
name: Hopper-v3 |
|
type: Hopper-v3 |
|
--- |
|
|
|
# **TD3** Agent playing **Hopper-v3** |
|
This is a trained model of a **TD3** agent playing **Hopper-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 td3 --env Hopper-v3 -orga sb3 -f logs/ |
|
python enjoy.py --algo td3 --env Hopper-v3 -f logs/ |
|
``` |
|
|
|
## Training (with the RL Zoo) |
|
``` |
|
python train.py --algo td3 --env Hopper-v3 -f logs/ |
|
# Upload the model and generate video (when possible) |
|
python -m rl_zoo3.push_to_hub --algo td3 --env Hopper-v3 -f logs/ -orga sb3 |
|
``` |
|
|
|
## Hyperparameters |
|
```python |
|
OrderedDict([('batch_size', 256), |
|
('gradient_steps', 1), |
|
('learning_rate', 0.0003), |
|
('learning_starts', 10000), |
|
('n_timesteps', 1000000.0), |
|
('policy', 'MlpPolicy'), |
|
('train_freq', 1), |
|
('normalize', False)]) |
|
``` |
|
|
