no code implementations • 25 May 2024 • Chun-Mao Lai, Hsiang-Chun Wang, Ping-Chun Hsieh, Yu-Chiang Frank Wang, Min-Hung Chen, Shao-Hua Sun
Inspired by the recent dominance of diffusion models in generative modeling, this work proposes Diffusion-Reward Adversarial Imitation Learning (DRAIL), which integrates a diffusion model into GAIL, aiming to yield more precise and smoother rewards for policy learning.
no code implementations • 26 Feb 2023 • Hsiang-Chun Wang, Shang-Fu Chen, Ming-Hao Hsu, Chun-Mao Lai, Shao-Hua Sun
Most existing imitation learning methods that do not require interacting with environments either model the expert distribution as the conditional probability p(a|s) (e. g., behavioral cloning, BC) or the joint probability p(s, a).