File size: 1,055 Bytes
91234a1 7ee9df9 91234a1 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 |
---
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))
```
|