Instructions to use Junfeel/pusher-v5-pro with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- stable-baselines3
How to use Junfeel/pusher-v5-pro with stable-baselines3:
from huggingface_sb3 import load_from_hub checkpoint = load_from_hub( repo_id="Junfeel/pusher-v5-pro", filename="{MODEL FILENAME}.zip", ) - Notebooks
- Google Colab
- Kaggle
PPO Agent for Gymnasium MuJoCo Pusher-v5
This model is a high-performance PPO agent controlling a 7-DOF robotic arm to push a cylinder towards a target in Gymnasium MuJoCo Pusher-v5.
Trained and exported directly via MuJoCo Pusher RL Studio Pro.
π¦Ύ Environment Details
- Environment:
Pusher-v5(Gymnasium / MuJoCo) - Algorithm: PPO (Stable-Baselines3)
- Observation Space: 23 dimensions (joint angles, velocities, tip, object, goal coordinates)
- Action Space: 7 dimensions (torque controls in range [-2.0, +2.0] Nm)
π Usage (Stable-Baselines3)
import gymnasium as gym
from stable_baselines3 import PPO
env = gym.make("Pusher-v5", render_mode="human")
model = PPO.load("ppo_pusher_latest.zip")
obs, _ = env.reset()
for _ in range(1000):
action, _ = model.predict(obs, deterministic=True)
obs, reward, done, truncated, _ = env.step(action)
if done or truncated:
obs, _ = env.reset()
env.close()
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