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---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: TD3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
metrics:
- type: mean_reward
value: 3204.34 +/- 882.86
name: mean_reward
verified: false
---
# **TD3** Agent playing **AntBulletEnv-v0**
This is a trained model of a **TD3** agent playing **AntBulletEnv-v0**
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
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo td3 --env AntBulletEnv-v0 -orga qgallouedec -f logs/
python -m rl_zoo3.enjoy --algo td3 --env AntBulletEnv-v0 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo td3 --env AntBulletEnv-v0 -orga qgallouedec -f logs/
python -m rl_zoo3.enjoy --algo td3 --env AntBulletEnv-v0 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo td3 --env AntBulletEnv-v0 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo td3 --env AntBulletEnv-v0 -f logs/ -orga qgallouedec
```
## Hyperparameters
```python
OrderedDict([('buffer_size', 200000),
('gamma', 0.98),
('gradient_steps', -1),
('learning_rate', 0.001),
('learning_starts', 10000),
('n_timesteps', 1000000.0),
('noise_std', 0.1),
('noise_type', 'normal'),
('policy', 'MlpPolicy'),
('policy_kwargs', 'dict(net_arch=[400, 300])'),
('train_freq', [1, 'episode']),
('normalize', False)])
```
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