aweeraman/reinforcement-learning-continuous-control
Continuous Control with deep reinforcement learning where the agent must reach a moving ball with a double jointed arm
This project demonstrates how a robotic arm can learn to track and make contact with a moving ball through trial and error. It takes observations about the arm's position, rotation, and velocity, and outputs torques to apply to its joints. This tool is for researchers and developers exploring how to teach robotic systems complex, continuous movement tasks.
No commits in the last 6 months.
Use this if you are studying reinforcement learning for robotics or continuous control problems and need a practical example of an agent learning fine-tuned movements.
Not ideal if you need a solution for discrete control problems or are looking for a pre-built, ready-to-deploy robotic control system.
Stars
9
Forks
1
Language
Python
License
—
Category
Last pushed
Feb 10, 2019
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/aweeraman/reinforcement-learning-continuous-control"
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