roboserg/RoboLeague
A car soccer environment inspired by Rocket League for deep reinforcement learning experiments in an adversarial self-play setting.
What it solves
It provides a set of reinforcement learning (RL) environments for testing vehicle control, specifically focusing on hovering and aerial ball handling.
How it works
The project uses the Unity ML-Agents toolkit to create simulation scenes where agents can be trained to perform specific tasks. It includes pre-defined scenes for basic ML tests, calm hovering, aerial dribbling, and maximizing speed with a constant roll.
Who it’s for
Developers and researchers interested in training machine learning agents for vehicle dynamics and aerial maneuvers within the Unity engine.
Highlights
- Training environments for aerial ball handling (AirDribble).
- Dedicated scenes for hovering and speed tests.
- Support for trained ONNX models.
- Built using Unity ML-Agents Release 11.
Related
- Project
- Project
- Project
- Project