Work

A Two-Stage Strategy Integrating Gaussian Processes and TD3 for Leader–Follower Coordination in Multi-Agent Systems

Year: 2025

Type: article

Abstract: In mobile multi-agent systems (MASs), achieving effective leader–follower coordination under unknown dynamics poses significant challenges. This study proposes a two-stage cooperative strategy that integrates Gaussian Processes (GPs) for modeling and a Twin Delayed Deep Deterministic Policy Gradient (TD3) for policy optimization (GPTD3), aiming to enhance adaptability and multi-objective optimization. Initially, GPs are utilized to model the uncertain dynamics of agents based on sensor data, providing a stable and noiseless training virtual environment for the first phase of TD3 strategy network training. Subsequently, a TD3-based compensation learning mechanism is introduced to reduce consensus errors among multiple agents by incorporating the position state of other agents. Additionally, the approach employs an enhanced dual-layer reward mechanism tailored to different stages of learning, ensuring robustness and improved convergence speed. Experimental results using a differential drive robot simulation demonstrate the superiority of this method over traditional controllers. The integration of the TD3 compensation network further improves the cooperative reward among agents.

Source: Journal of Sensor and Actuator Networks

DOI: 10.3390/jsan14030051

Authors: Xicheng Zhang, Bingchun Jiang, Fuqin Deng, Min Zhao

Institutions: Wuyi University, Guangdong University of Science and Technology, Wuyi University

Language: English

Topic: Reinforcement Learning in Robotics

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,413

Funders: Wuyi University, Macau University of Science and Technology