
# **A2C** Agent playing **Walker2d-v3**
This is a trained model of a **A2C** 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 a2c --env Walker2d-v3 -orga sb3 -f logs/
python enjoy.py --algo a2c --env Walker2d-v3  -f logs/
```

## Training (with the RL Zoo)
```
python train.py --algo a2c --env Walker2d-v3 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo a2c --env Walker2d-v3 -f logs/ -orga sb3
```

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

{"library_name": "stable-baselines3", "tags": ["Walker2d-v3", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "A2C", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Walker2d-v3", "type": "Walker2d-v3"}, "metrics": [{"type": "mean_reward", "value": "577.46 +/- 43.78", "name": "mean_reward"}]}]}]}
stable-baselines3 Walker2d-v3 deep-reinforcement-learning reinforcement-learning model-index region:us
TAGS
#stable-baselines3 #Walker2d-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us 


# A2C Agent playing Walker2d-v3
This is a trained model of a A2C agent playing Walker2d-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



## Training (with the RL Zoo)


## Hyperparameters


# A2C Agent playing Walker2d-v3
This is a trained model of a A2C agent playing Walker2d-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 ## Training (with the RL Zoo) ## Hyperparameters
TAGS
#stable-baselines3 #Walker2d-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us 
 # A2C Agent playing Walker2d-v3
This is a trained model of a A2C agent playing Walker2d-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 ## Training (with the RL Zoo) ## Hyperparameters
passage: TAGS
#stable-baselines3 #Walker2d-v3 #deep-reinforcement-learning #reinforcement-learning #model-index #region-us 
# A2C Agent playing Walker2d-v3
This is a trained model of a A2C agent playing Walker2d-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## Training (with the RL Zoo)## Hyperparameters