Heterogeneous Multi-Agent Learning in Isaac Lab
Scalable multi-agent learning workflows in Isaac Lab for coordinated robotic collaboration.
Stack: Isaac Lab, PyTorch, Multi-Agent RL, Simulation
This work focuses on enabling heterogeneous multi-agent learning at scale inside Isaac Lab—supporting high-fidelity physics and parallelized training.
Key contributions:
- Multi-agent training/evaluation scaffolding
- Repeatable experiment pipelines
- Simulation-first iteration for coordinated behaviors