42 citations · 96 across the 12 of their papers we have counts for
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cs.AI2026
Language-based Trial and Error Falls Behind in the Era of Experience
Haoyu Wang, Guozheng Ma, Shugang Cui +7
While Large Language Models (LLMs) excel in language-based agentic tasks, their applicability to unseen, nonlinguistic environments (e.g., symbolic or spatial tasks) remains limite…
cs.AI2023★ 1 cited
Auto MC-Reward: Automated Dense Reward Design with Large Language Models for Minecraft
Hao Li, Xue Yang, Zhaokai Wang +7
Many reinforcement learning environments (e.g., Minecraft) provide only sparse rewards that indicate task completion or failure with binary values. The challenge in exploration eff…
cs.AI2023★ 24 cited
Ghost in the Minecraft: Generally Capable Agents for Open-World Environments via Large Language Models with Text-based Knowledge and Memory
Xizhou Zhu, Yuntao Chen, Hao Tian +10
The captivating realm of Minecraft has attracted substantial research interest in recent years, serving as a rich platform for developing intelligent agents capable of functioning…