
# **A2C** Agent playing **Ant-v3**
This is a trained model of a **A2C** agent playing **Ant-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

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 a2c --env Ant-v3 -orga qgallouedec -f logs/
python -m rl_zoo3.enjoy --algo a2c --env Ant-v3  -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 a2c --env Ant-v3 -orga qgallouedec -f logs/
python -m rl_zoo3.enjoy --algo a2c --env Ant-v3  -f logs/
```

## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo a2c --env Ant-v3 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo a2c --env Ant-v3 -f logs/ -orga qgallouedec
```

## Hyperparameters
```python
OrderedDict([('n_timesteps', 1000000.0),
             ('normalize', True),
             ('policy', 'MlpPolicy'),
             ('normalize_kwargs', {'norm_obs': True, 'norm_reward': False})])
```

{"library_name": "stable-baselines3", "tags": ["Ant-v3", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "A2C", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Ant-v3", "type": "Ant-v3"}, "metrics": [{"type": "mean_reward", "value": "-182.48 +/- 159.86", "name": "mean_reward", "verified": false}]}]}]}
stable-baselines3 Ant-v3 deep-reinforcement-learning reinforcement-learning model-index region:us
TAGS
#stable-baselines3 #Ant-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us 


# A2C Agent playing Ant-v3
This is a trained model of a A2C agent playing Ant-v3
using the stable-baselines3 library
and the RL 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: URL
SB3: URL
SB3 Contrib: URL

Install the RL Zoo (with SB3 and SB3-Contrib):




If you installed the RL Zoo3 via pip ('pip install rl_zoo3'), from anywhere you can do:


## Training (with the RL Zoo)


## Hyperparameters


# A2C Agent playing Ant-v3
This is a trained model of a A2C agent playing Ant-v3
using the stable-baselines3 library
and the RL 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: URL
SB3: URL
SB3 Contrib: URL

Install the RL Zoo (with SB3 and SB3-Contrib):




If you installed the RL Zoo3 via pip ('pip install rl_zoo3'), from anywhere you can do: ## Training (with the RL Zoo) ## Hyperparameters
TAGS
#stable-baselines3 #Ant-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us 
 # A2C Agent playing Ant-v3
This is a trained model of a A2C agent playing Ant-v3
using the stable-baselines3 library
and the RL 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: URL
SB3: URL
SB3 Contrib: URL

Install the RL Zoo (with SB3 and SB3-Contrib):




If you installed the RL Zoo3 via pip ('pip install rl_zoo3'), from anywhere you can do: ## Training (with the RL Zoo) ## Hyperparameters