Applied Sciences (May 2024)

Hybrid Centralized Training and Decentralized Execution Reinforcement Learning in Multi-Agent Path-Finding Simulations

  • Hua-Ching Chen,
  • Shih-An Li,
  • Tsung-Han Chang,
  • Hsuan-Ming Feng,
  • Yun-Chien Chen

DOI
https://doi.org/10.3390/app14103960
Journal volume & issue
Vol. 14, no. 10
p. 3960

Abstract

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In this paper, we propose a hybrid centralized training and decentralized execution neural network architecture with deep reinforcement learning (DRL) to complete the multi-agent path-finding simulation. In the training of physical robots, collisions and other unintended accidents are very likely to occur in multi-agent cases, so it is required to train the networks within a deep deterministic policy gradient for the virtual environment of the simulator. The simple particle multi-agent simulator designed by OpenAI (Sacramento, CA, USA) for training platforms can easily obtain the state information of the environment. The overall system of the training cycle is designed with a self-designed reward function and is completed through a progressive learning approach from a simple to a complex environment. Finally, we carried out and presented the experiments of multi-agent path-finding simulations. The proposed methodology is better than the multi-agent model-based policy optimization (MAMBPO) and model-free multi-agent soft actor–critic models.

Keywords