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---
library_name: stable-baselines3
tags:
- HopperBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- metrics:
- type: mean_reward
value: 709.34 +/- 213.24
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: HopperBulletEnv-v0
type: HopperBulletEnv-v0
---
# **A2C** Agent playing **HopperBulletEnv-v0**
This is a trained model of a **A2C** agent playing **HopperBulletEnv-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
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo a2c --env HopperBulletEnv-v0 -orga sb3 -f logs/
python enjoy.py --algo a2c --env HopperBulletEnv-v0 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo a2c --env HopperBulletEnv-v0 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo a2c --env HopperBulletEnv-v0 -f logs/ -orga sb3
```
## Hyperparameters
```python
OrderedDict([('ent_coef', 0.0),
('env_wrapper', 'sb3_contrib.common.wrappers.TimeFeatureWrapper'),
('gae_lambda', 0.9),
('gamma', 0.99),
('learning_rate', 'lin_0.00096'),
('max_grad_norm', 0.5),
('n_envs', 4),
('n_steps', 8),
('n_timesteps', 2000000.0),
('normalize', True),
('normalize_advantage', False),
('policy', 'MlpPolicy'),
('policy_kwargs', 'dict(log_std_init=-2, ortho_init=False)'),
('use_rms_prop', True),
('use_sde', True),
('vf_coef', 0.4),
('normalize_kwargs', {'norm_obs': True, 'norm_reward': False})])
```
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