Swarm intelligence capture-the-flag game with imperfect information based on deep reinforcement learning
Jianrui Wang, Jiahao Huang, Yang Shan Tang · Scientia Sinica Technologica · 2022
One of the major research areas has been the problem of swarm intelligence games in complex environments. This study offers G-MAD3QN, a multi-agent deep reinforcement learning system based on Multi-agent Dueling Double Deep Q-Network (MAD3QN) and Graph Attention Network (GAT), to handle the challenges of multi-agent capture-the-flag games under imperfect information settings. The algorithm realizes the path planning in the labyrinth map while also modeling the cooperation and competition relationships of multi-agents under imperfect information conditions at the same time so as to determine the strategy of the capture the flag game. In the experiment, we consider the imperfect observation information of the agents based on the two-dimensional maze environment. Moreover, in the two-on-two capture-the-flag game, we compared the G-MAD3QN algorithm to baseline multi-agent deep reinforcement learning algorithms, such as Multi-agent Deep Q-Network (MADQN) and MAD3QN, to verify the proposed algorithm’s effectiveness.