---
library_name: stable-baselines3
tags:
- Pendulum-v1
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
  results:
  - metrics:
    - type: mean_reward
      value: -272.21 +/- 159.73
      name: mean_reward
    task:
      type: reinforcement-learning
      name: reinforcement-learning
    dataset:
      name: Pendulum-v1
      type: Pendulum-v1
---

  # **PPO** Agent playing **Pendulum-v1**
  This is a trained model of a **PPO** agent playing **Pendulum-v1** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
  
  ## Usage (with Stable-baselines3)
```python
from stable_baselines3 import PPO
from stable_baselines3.common.env_util import make_vec_env

# Create the environment
env_id = "Pendulum-v1"
env = make_vec_env(env_id, n_envs=1)

# Instantiate the agent
model = PPO(
    "MlpPolicy",
    env,
    gamma=0.98,
    use_sde=True,
    sde_sample_freq=4,
    learning_rate=1e-3,
    verbose=1,
)

# Train the agent
model.learn(total_timesteps=int(1e5))

```