Knowing What Not to Do: Leverage Language Model Insights for Action
Space Pruning in Multi-agent Reinforcement Learning

Knowing What Not to Do: Leverage Language Model Insights for Action Space Pruning in Multi-agent Reinforcement Learning

27 May 2024

Lei Song

Papers citing "Knowing What Not to Do: Leverage Language Model Insights for Action Space Pruning in Multi-agent Reinforcement Learning"

8 / 8 papers shown

Title
MARLIN: Multi-Agent Reinforcement Learning Guided by Language-Based Inter-Robot Negotiation Toby Godfrey William Hunt Mohammad D. Soorati 56 1 0 18 Oct 2024
Verco: Learning Coordinated Verbal Communication for Multi-agent Reinforcement Learning Dapeng Li Hang Dong Lu Wang Bo Qiao Si Qin ... Dongmei Zhang Qi Zhang Zhiwei Xu Bin Zhang Guoliang Fan 46 4 0 27 Apr 2024
DuaLight: Enhancing Traffic Signal Control by Leveraging Scenario-Specific and Scenario-Shared Knowledge Jiaming Lu Jingqing Ruan Haoyuan Jiang Ziyue Li Hangyu Mao Rui Zhao 29 10 0 22 Dec 2023
From Explicit Communication to Tacit Cooperation:A Novel Paradigm for Cooperative MARL Dapeng Li Zhiwei Xu Bin Zhang Guoliang Fan 37 7 0 28 Apr 2023
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 233 2,413 0 06 Oct 2022
Skill Induction and Planning with Latent Language Pratyusha Sharma Antonio Torralba Jacob Andreas LM&Ro 194 108 0 04 Oct 2021
Is Curiosity All You Need? On the Utility of Emergent Behaviours from Curious Exploration Oliver Groth Markus Wulfmeier Giulia Vezzani Vibhavari Dasagi Tim Hertweck Roland Hafner N. Heess Martin Riedmiller LRM 31 20 0 17 Sep 2021
ROMA: Multi-Agent Reinforcement Learning with Emergent Roles Tonghan Wang Heng Dong V. Lesser Chongjie Zhang 51 210 0 18 Mar 2020