Adversarial Multi-Agent RL Framework in IsaacLab
A scalable framework for heterogeneous multi-agent adversarial RL in high-fidelity simulation.
Stack: Isaac Lab, PyTorch, Adversarial RL, Multi-Agent RL
A framework-oriented project aimed at making heterogeneous multi-agent adversarial learning reproducible and scalable in Isaac Lab.
Highlights:
- Heterogeneous agent support
- Scalable training configs
- Clear separation between environment, policies, and evaluation