MPC-Berkeley/Implicit-Game-Theoretic-MPC
Implicit Game-Theoretic MPC
This project helps autonomous vehicle engineers develop control strategies for multi-agent scenarios like racing or navigating intersections. It takes in vehicle state data and desired behaviors, then outputs optimized control commands that allow multiple vehicles to interact safely and efficiently. An autonomous vehicle controls engineer or a robotics researcher would find this useful for designing and testing new algorithms.
No commits in the last 6 months.
Use this if you are designing control systems for autonomous vehicles that need to interact with other vehicles in complex scenarios like racing or navigating un-signalized intersections.
Not ideal if you are working with single-agent control problems or require real-time deployment on production systems without further engineering.
Stars
20
Forks
2
Language
Python
License
—
Category
Last pushed
Feb 28, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/MPC-Berkeley/Implicit-Game-Theoretic-MPC"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
LucasAlegre/sumo-rl
Reinforcement Learning environments for Traffic Signal Control with SUMO. Compatible with...
hilo-mpc/hilo-mpc
HILO-MPC is a Python toolbox for easy, flexible and fast development of...
reiniscimurs/DRL-robot-navigation
Deep Reinforcement Learning for mobile robot navigation in ROS Gazebo simulator. Using Twin...
kyegomez/RoboCAT
Implementation of Deepmind's RoboCat: "Self-Improving Foundation Agent for Robotic Manipulation"...
OpenQuadruped/spot_mini_mini
Dynamics and Domain Randomized Gait Modulation with Bezier Curves for Sim-to-Real Legged Locomotion.