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---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 2447.40 +/- 23.14
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
---
# **PPO** Agent playing **AntBulletEnv-v0**
This is a trained model of a **PPO** agent playing **AntBulletEnv-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
MODEL
model = PPO(policy = "MlpPolicy",
env = env,
batch_size = 256,
clip_range = 0.4,
ent_coef = 0.0,
gae_lambda = 0.92,
gamma = 0.99,
learning_rate = 3.0e-05,
max_grad_norm = 0.5,
n_epochs = 30,
n_steps = 512,
policy_kwargs = dict(log_std_init=-2, ortho_init=False, activation_fn=nn.ReLU, net_arch=[dict(pi=[256,
256], vf=[256, 256])] ),
use_sde = True,
sde_sample_freq = 4,
vf_coef = 0.5,
tensorboard_log = "./tensorboard",
verbose=1)
model.learn(1_000_000) |