Jiamin Shi — accepted AI-conference papers

2025

Modeling Human-like Driving Behavior Based on Maximum Entropy Deep Inverse Reinforcement Learning

Jiamin Shi, Tangyike Zhang, Shitao Chen, Nanning Zheng, Jingmin Xin

IROS 2025

Modeling expert driving behavior is crucial for the successful implementation of human-like autonomous driving. In this paper, we propose a new sampling-based Maximum Entropy Deep Inverse Reinforcement Learning (MEDIRL) framework. It leverages naturalistic human driving data to train the reward mode

Cited by 1 Source ↗Scholar ↗

2024

Task-Driven Autonomous Driving: Balanced Strategies Integrating Curriculum Reinforcement Learning and Residual Policy

Jiamin Shi, Tangyike Zhang, Ziqi Zong, Shitao Chen, Jingmin Xin, Nanning Zheng

RA-L 2024

Achieving fully autonomous driving in urban traffic scenarios is a significant challenge that necessitates balancing safety, efficiency, and compliance with traffic regulations. In this letter, we introduce a novel Curriculum Residual Hierarchical Reinforcement Learning (CR-HRL) framework. It integr

Cited by 6 Source ↗Scholar ↗