A Collaborative Multi-Agent Reinforcement Learning Approach for Non-Stationary Environments with Unknown Change Points

A Collaborative Multi-Agent Reinforcement Learning Approach for Non-Stationary Environments with Unknown Change Points

Reinforcement learning has achieved significant success in sequential decision-making problems but exhibits poor adaptability in non-stationary environments with unknown dynamics, a challenge particularly pronounced in multi-agent scenarios. This study aims to enhance the adaptive capability of mult...

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Bibliographic Details
Main Authors: Suyu Wang, Quan Yue, Zhenlei Xu, Peihong Qiao, Zhentao Lyu, Feng Gao
Format: Article
Language:English
Published: MDPI AG 2025-05-01
Series:Mathematics
Subjects:
non-stationary environments
multi-agent reinforcement learning
unknown change points
composite experience replay buffer
adaptive exploration
Online Access:https://www.mdpi.com/2227-7390/13/11/1738
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