3 citations · 3 across the 4 of their papers we have counts for
4 papers
ImagineBench: Evaluating Reinforcement Learning with Large Language Model Rollouts
Jing-Cheng Pang, Kaiyuan Li, Yidi Wang +3
A central challenge in reinforcement learning (RL) is its dependence on extensive real-world interaction data to learn task-specific policies. While recent work demonstrates that l…
Knowledgeable Agents by Offline Reinforcement Learning from Large Language Model Rollouts
Jing-Cheng Pang, Si-Hang Yang, Kaiyuan Li +4
Reinforcement learning (RL) trains agents to accomplish complex tasks through environmental interaction data, but its capacity is also limited by the scope of the available data. T…
Empowering Language Models with Active Inquiry for Deeper Understanding
Jing-Cheng Pang, Heng-Bo Fan, Pengyuan Wang +6
The rise of large language models (LLMs) has revolutionized the way that we interact with artificial intelligence systems through natural language. However, LLMs often misinterpret…
Imitator Learning: Achieve Out-of-the-Box Imitation Ability in Variable Environments
Xiong-Hui Chen, Junyin Ye, Hang Zhao +9
Imitation learning (IL) enables agents to mimic expert behaviors. Most previous IL techniques focus on precisely imitating one policy through mass demonstrations. However, in many…