ProfessorNova/PPO-Humanoid
PPO implementation for controlling a humanoid in Gymnasium's Mujoco environment, featuring customizable training scripts and multi-environment parallel training.
This project helps robotics researchers and AI trainers develop and test advanced locomotion for humanoid robots in simulated environments. It takes raw environment data from the simulated robot's sensors and outputs optimized control policies, allowing the humanoid to perform complex movements like walking and running. This is ideal for those focused on creating realistic and efficient humanoid robot behaviors.
Use this if you are developing or experimenting with deep reinforcement learning to achieve complex, stable, and fast locomotion for humanoid robots in a simulated environment.
Not ideal if you need to control physical robots or if your primary interest is in areas other than reinforcement learning for simulated humanoid locomotion.
Stars
31
Forks
3
Language
Python
License
—
Category
Last pushed
Oct 20, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/ProfessorNova/PPO-Humanoid"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
DLR-RM/stable-baselines3
PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
google-deepmind/dm_control
Google DeepMind's software stack for physics-based simulation and Reinforcement Learning...
Denys88/rl_games
RL implementations
pytorch/rl
A modular, primitive-first, python-first PyTorch library for Reinforcement Learning.
yandexdataschool/Practical_RL
A course in reinforcement learning in the wild