Himchan Hwang — accepted AI-conference papers

2025

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

Seokjin Choi, Yonghyeon Lee, Seungyeon Kim, Che-Sang Park, Himchan Hwang, Frank C. Park

RA-L 2025

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

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2024

Maximum Entropy Inverse Reinforcement Learning of Diffusion Models with Energy-Based Models

Sangwoong Yoon, Himchan Hwang, Dohyun Kwon, Yung-Kyun Noh, Frank C. Park

NeurIPS 2024oral

We present a maximum entropy inverse reinforcement learning (IRL) approach for improving the sample quality of diffusion generative models, especially when the number of generation time steps is small. Similar to how IRL trains a policy based on the reward function learned from expert demonstrations…

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