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---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
metrics:
- type: mean_reward
value: 1834.41 +/- 107.15
name: mean_reward
verified: false
---
# **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)
TODO: Add your code
```python
import pybullet_envs
import panda_gym
import gym
import os
from huggingface_sb3 import load_from_hub, package_to_hub
from stable_baselines3 import A2C
from stable_baselines3.common.evaluation import evaluate_policy
from stable_baselines3.common.vec_env import DummyVecEnv, VecNormalize
from stable_baselines3.common.env_util import make_vec_env
from huggingface_hub import notebook_login
#Environment 1: AntBulletEnv-v0
env_id = "AntBulletEnv-v0"
# Create the env
env = gym.make(env_id)
env = make_vec_env(env_id, n_envs=4)
# Adding this wrapper to normalize the observation and the reward
env = VecNormalize(env, norm_obs=True, norm_reward=True, clip_obs=10)
#create A2C model
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,
seed=11,
policy_kwargs=dict(
log_std_init=-2, ortho_init=False),
normalize_advantage=False,
use_rms_prop= True,
use_sde= True,
verbose=1)
#train agent
model.learn(1_500_000)
# Save the model and VecNormalize statistics when saving the agent
model.save("a2c-AntBulletEnv-v0")
env.save("vec_normalize.pkl")
```
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