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cs.LG2025
Residual Policy Gradient: A Reward View of KL-regularized Objective
Pengcheng Wang, Xinghao Zhu, Yuxin Chen +3
Reinforcement Learning and Imitation Learning have achieved widespread success in many domains but remain constrained during real-world deployment. One of the main issues is the ad…
cs.LG2023★ 3 cited
Residual Q-Learning: Offline and Online Policy Customization without Value
Chenran Li, Chen Tang, Haruki Nishimura +3
Imitation Learning (IL) is a widely used framework for learning imitative behavior from demonstrations. It is especially appealing for solving complex real-world tasks where handcr…