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---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- metrics:
- type: mean_reward
value: 1218.38 +/- 203.74
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
---
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
## parameters
```python
model = A2C(policy = "MlpPolicy",
env = env,
gae_lambda = 0.9,
gamma = 0.99,
learning_rate = 0.00096,
max_grad_norm = 0.5,
n_steps = 8,
vf_coef = 0.4,
ent_coef = 0.0,
tensorboard_log = "./tensorboard",
policy_kwargs=dict(
log_std_init=-2, ortho_init=False),
normalize_advantage=False,
use_rms_prop= True,
use_sde= True,
verbose=1)
...
```
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