Abstract
This article addresses the problem of managing multi-agent robotic systems using artificial intelligence technologies. A hybrid model combining the Vision-Language-Action (VLA) architecture with the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm is proposed for the synergetic control of a group of robots. The model is built on the Centralized Training with Decentralized Execution (CTDE) principle, with a Social-Force component incorporated into the reward function to automatically maintain a safe distance between agents. Experiments were conducted in the NVIDIA Isaac Sim environment using a dataset of over 10,000 trajectories and scenarios involving 4 to 16 agents. The results show a 90% task success rate, a 0.5% collision probability, and 89% energy efficiency, along with a significant reduction in communication load compared to a rule-based method and standard MADDPG. These findings confirm that integrating VLA models with multi-agent reinforcement learning improves the adaptability, fault tolerance, and collective decision-making capability of robotic swarms, opening prospects for application in agriculture, logistics, and smart-city infrastructure.
First Page
51
Last Page
57
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Recommended Citation
Hamiyev, A.T.
(2026)
"MANAGEMENT OF MULTI-AGENT ROBOTIC SYSTEMS USING ARTIFICIAL INTELLIGENCE TECHNOLOGIES,"
Chemical Technology, Control and Management: Vol. 2026:
Iss.
3, Article 6.
DOI: https://doi.org/10.59048/2181-1105.1809
