首页|Active legibility in multiagent reinforcement learning

Active legibility in multiagent reinforcement learning

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  • NETL
  • NSTL
  • Elsevier
A multiagent sequential decision problem has been seen in many critical applications including urban transportation, autonomous driving cars, military operations, etc. Its widely known solution, namely multiagent reinforcement learning, has evolved tremendously in recent years. Among them, the solution paradigm of modeling other agents attracts our interest, which is different from traditional value decomposition or communication mechanisms. It enables agents to understand and anticipate others' behaviors and facilitates their collaboration. Inspired by recent research on the legibility that allows agents to reveal their intentions through their behavior, we propose a multiagent active legibility framework to improve their performance. The legibility-oriented framework drives agents to conduct legible actions so as to help others optimize their behaviors. In addition, we design a series of problem domains that emulate a common legibility-needed scenario and effectively characterize the legibility in multiagent reinforcement learning. The experimental results demonstrate that the new framework is more efficient and requires less training time compared to several multiagent reinforcement learning algorithms.

LegibilityMultiagent reinforcement learningMultiagent interaction

Yanyu Liu、Yinghui Pan、Yifeng Zeng、Biyang Ma、Prashant Doshi

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School of Automation, Central South University, No. 605 South Lushan Road, Changsha, 410083, Hunan, China

School of Artificial Intelligence & National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University, Shenzhen, Guangdong, China

Department of Computer and Information Sciences, Northumbria University, Newcastle, United Kingdom

School of Computer Science, Minnan Normal University, Zhangzhou, Fujian, China

Department of Computer Science, University of Georgia, Athens, GA, USA

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2025

10.1016/j.artint.2025.104357

Artificial intelligence

Artificial intelligence

SCI
ISSN：0004-3702
年,卷(期)：2025.346(Sep.)
  • 51