3 papers
stat.ML2025
On Generalization and Distributional Update for Mimicking Observations with Adequate Exploration
Yirui Zhou, Yunfei Jin, Xiaowei Liu +2
Learning from observations (LfO) replicates expert behavior without needing access to the expert's actions, making it more practical than learning from demonstrations (LfD) in many…
stat.ML2024
On Reward Transferability in Adversarial Inverse Reinforcement Learning: Insights from Random Matrix Theory
Yangchun Zhang, Wang Zhou, Yirui Zhou
In the context of inverse reinforcement learning (IRL) with a single expert, adversarial inverse reinforcement learning (AIRL) serves as a foundational approach to providing compre…
cs.LG2024
Rethinking Adversarial Inverse Reinforcement Learning: Policy Imitation, Transferable Reward Recovery and Algebraic Equilibrium Proof
Yangchun Zhang, Qiang Liu, Weiming Li +1
Adversarial inverse reinforcement learning (AIRL) stands as a cornerstone approach in imitation learning, yet it faces criticisms from prior studies. In this paper, we rethink AIRL…