most citedCLARE: Conservative Model-Based Reward Learning for Offline Inverse Reinforcement Learning

6 citations · 6 across the 2 of their papers we have counts for

collaborators

2 papers

cs.CL2024

Code-Based English Models Surprising Performance on Chinese QA Pair Extraction Task

Linghan Zheng, Hui Liu, Xiaojun Lin +5

In previous studies, code-based models have consistently outperformed text-based models in reasoning-intensive scenarios. When generating our knowledge base for Retrieval-Augmented…

cs.LG2023★ 6 cited

CLARE: Conservative Model-Based Reward Learning for Offline Inverse Reinforcement Learning

Sheng Yue, Guanbo Wang, Wei Shao +4

This work aims to tackle a major challenge in offline Inverse Reinforcement Learning (IRL), namely the reward extrapolation error, where the learned reward function may fail to exp…