5 papers · 1 filter
Quantile Q-Learning: Revisiting Offline Extreme Q-Learning with Quantile Regression
Xinming Gao, Shangzhe Li, Yujin Cai +1
Offline reinforcement learning (RL) enables policy learning from fixed datasets without further environment interaction, making it particularly valuable in high-risk or costly doma…
A Recipe for Efficient Sim-to-Real Transfer in Manipulation with Online Imitation-Pretrained World Models
Yilin Wang, Shangzhe Li, Haoyi Niu +4
We are interested in solving the problem of imitation learning with a limited amount of real-world expert data. Existing offline imitation methods often struggle with poor data cov…
Near-Optimal Second-Order Guarantees for Model-Based Adversarial Imitation Learning
Shangzhe Li, Dongruo Zhou, Weitong Zhang
We study online adversarial imitation learning (AIL), where an agent learns from offline expert demonstrations and interacts with the environment online without access to rewards.…
Language Model Distillation: A Temporal Difference Imitation Learning Perspective
Zishun Yu, Shangzhe Li, Xinhua Zhang
Large language models have led to significant progress across many NLP tasks, although their massive sizes often incur substantial computational costs. Distillation has become a co…
Coupled Distributional Random Expert Distillation for World Model Online Imitation Learning
Shangzhe Li, Zhiao Huang, Hao Su
Imitation Learning (IL) has achieved remarkable success across various domains, including robotics, autonomous driving, and healthcare, by enabling agents to learn complex behavior…