EnvGen: Generating and Adapting Environments via LLMs for Training
Embodied Agents

EnvGen: Generating and Adapting Environments via LLMs for Training Embodied Agents

18 March 2024

Mohit Bansal

Papers citing "EnvGen: Generating and Adapting Environments via LLMs for Training Embodied Agents"

7 / 7 papers shown

Title
Multi-agent Embodied AI: Advances and Future Directions Zhaohan Feng Ruiqi Xue Lei Yuan Yang Yu Ning Ding M. Liu Bingzhao Gao Jian-jun Sun Gang Wang AI4CE 42 0 0 08 May 2025
DataEnvGym: Data Generation Agents in Teacher Environments with Student Feedback Zaid Khan Elias Stengel-Eskin Jaemin Cho Mohit Bansal VGen 31 1 0 08 Oct 2024
ReAct: Synergizing Reasoning and Acting in Language Models Shunyu Yao Jeffrey Zhao Dian Yu Nan Du Izhak Shafran Karthik Narasimhan Yuan Cao LLMAG ReLM LRM 208 2,413 0 06 Oct 2022
Large Language Models are Zero-Shot Reasoners Takeshi Kojima S. Gu Machel Reid Yutaka Matsuo Yusuke Iwasawa ReLM LRM 291 2,712 0 24 May 2022
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models Jason W. Wei Xuezhi Wang Dale Schuurmans Maarten Bosma Brian Ichter F. Xia Ed H. Chi Quoc Le Denny Zhou LM&Ro LRM AI4CE ReLM 315 8,261 0 28 Jan 2022
Situated Dialogue Learning through Procedural Environment Generation Prithviraj Ammanabrolu Renee Jia Mark O. Riedl 95 14 0 07 Oct 2021
Replay-Guided Adversarial Environment Design Minqi Jiang Michael Dennis Jack Parker-Holder Jakob N. Foerster Edward Grefenstette Tim Rocktaschel 116 94 0 06 Oct 2021