Title: Hybrid group formation simulation based on deep reinforcement learning

Authors: Nahid Salehi; Hossein Mahvash Mohammadi; Mankyu Sung

Addresses: Department of Computer Engineering, Isfahan University, Isfahan, 8174673441, Iran ' Department of Computer Engineering, Isfahan University, Isfahan, 8174673441, Iran ' Department of Game and Mobile, Keimyung University, Daegu, 8174673441, South Korea

Abstract: A group formation problem is defined as the simulation of groups of agents, moving without collision while forming a specific shape. The development of this type of problem is usually done using velocity-based or deep-reinforcement learning methods. In velocity-based methods, it is possible to create complex environments with more realistic behaviours of the agents in the environment. However, the computational complexity and inflexibility in changing the formation are among the leading challenges. Using velocity-based and deep reinforcement learning techniques, agents learn to have a collision-free motion in the desired formations. The proposed algorithm, we called 'DGB DRL', takes advantage of a hybrid method by combining the two approaches as a formation control algorithm. The evaluation results of the proposed method show an improvement in reducing computational complexity and increasing flexibility in complex environments.

Keywords: multi-agent simulation; deep reinforcement learning; DRL; discretionary group behaviour; group formation; velocity-based avoidance methods.

DOI: 10.1504/IJISTA.2024.139742

International Journal of Intelligent Systems Technologies and Applications, 2024 Vol.22 No.2, pp.151 - 172

Received: 20 May 2022
Accepted: 19 Sep 2023
Published online: 05 Jul 2024 *

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