Che-Sang Park

Diverse Policy Learning via Random Obstacle Deployment for Zero-Shot Adaptation

In this letter, we propose a novel reinforcement learning framework that enables zero-shot policy adaptation in environments with unseen, dynamically changing obstacles. Adopting the idea that learning a policy capable of generating diverse actions is key to achieving such adaptability, our primary