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hwihwalab/neuromotion-humanoid-v5-ppo
neuromotion-humanoid-v5-ppo is a reinforcement learning model from hwihwalab. Use it for the reinforcement learning task on the model card, and read the license before you ship it in a product. It is set up for stable-baselines3. The card lists the license as mit.
[](https://huggingface.co/hwihwalab/neuromotion-humanoid-v5-ppo/blob/main/README.md) [](https://huggingface.co/hwihwalab/neuromotion-humanoid-v5-ppo/blob/main/READMEKR.md) [](https://huggingface.co/hwihwalab/neuromoti…
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From the Hugging Face model README
MuJoCo Humanoid-v5 Continuous Control Telemetry & PPO Reinforcement Learning System
🌐 English Documentation | 🇰🇷 한국어 매뉴얼
This repository contains an advanced continuous reinforcement learning system (PPO) and a real-time engineering telemetry dashboard for bipedal robotic continuous control in Gymnasium MuJoCo Humanoid-v5.
| Parameter | Specification |
|---|---|
| Environment | Gymnasium MuJoCo Humanoid-v5 |
| Observation Space | 378-dimensional continuous vector |
| Action Space | 17-dimensional continuous joint torques (Box[-1.0, 1.0]) |
| Algorithm | Proximal Policy Optimization (PPO) |
| Framework | Stable-Baselines3 / PyTorch |
| Architecture | Actor-Critic MLP Policy (MlpPolicy) |
| Average Survival Steps | 88.5 steps (Peak: 109 steps) |
| Average Cumulative Reward | +455.52 (Peak: +549.18) |
import gymnasium as gym
from stable_baselines3 import PPO
from huggingface_hub import hf_hub_download
# Download model weights from Hugging Face Hub
model_file = hf_hub_download(
repo_id="hwihwalab/neuromotion-humanoid-v5-ppo",
filename="humanoid_ppo_model.zip"
)
# Initialize MuJoCo Humanoid-v5 environment
env = gym.make("Humanoid-v5", render_mode="human")
model = PPO.load(model_file, env=env)
# Run evaluation episodes
obs, info = env.reset()
for _ in range(1000):
action, _states = model.predict(obs, deterministic=True)
obs, reward, terminated, truncated, info = env.step(action)
if terminated or truncated:
obs, info = env.reset()
env.close()
The repository also includes a native 60fps Pygame telemetry cockpit:
python run_gui.py
README.md: Hugging Face model card, specifications, and telemetry manual.README_KR.md: Dedicated Korean manual (한국어 매뉴얼).humanoid_ppo_model.zip: Pre-trained PPO neural network weights.simulation_engine.py: Gymnasium MuJoCo physical engine & 17-DOF torque extractor.train_rl.py: Stable-Baselines3 PPO incremental trainer & diagnostics.run_gui.py: Pygame real-time telemetry cockpit.mujoko_humanoid.py: Multi-mode CLI execution and visualization script.This project is licensed under the MIT License - see the LICENSE file for details.
Trained and deployed with NeuroMotion Studio by hwihwalab.