rickstaa/stable-gym
This package contains several gymnasium environments with positive definite cost functions, designed for compatibility with stable RL agents.
This package provides ready-to-use simulation environments for developing and testing robust reinforcement learning agents. It takes common control problems and outputs environments configured specifically for algorithms that need stable performance guarantees. Developers building or evaluating stable reinforcement learning agents will find this useful.
No commits in the last 6 months.
Use this if you are a machine learning engineer or researcher developing stable reinforcement learning algorithms and need pre-built, compatible simulation environments.
Not ideal if you are looking for general-purpose reinforcement learning environments without specific stability requirements or are not working with Python.
Stars
12
Forks
2
Language
Python
License
MIT
Category
Last pushed
Sep 04, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/rickstaa/stable-gym"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
qgallouedec/panda-gym
Set of robotic environments based on PyBullet physics engine and gymnasium.
nicrusso7/rex-gym
OpenAI Gym environments for an open-source quadruped robot (SpotMicro)
amazon-science/auction-gym
AuctionGym is a simulation environment that enables reproducible evaluation of bandit and...
upb-lea/openmodelica-microgrid-gym
OpenModelica Microgrid Gym (OMG): An OpenAI Gym Environment for Microgrids
vietnh1009/Super-mario-bros-A3C-pytorch
Asynchronous Advantage Actor-Critic (A3C) algorithm for Super Mario Bros