@inproceedings{8557fff4331649b78859e3e723168fee,
title = "Online Model-Pool Selection and Fusion for Adaptive MARL in Wargames",
abstract = "To address the critical need for robust and accurate decision-making in complex, real-time environments, we propose a lightweight decision framework that dynamically selects the best candidate from an algorithm pool comprising Convolutional Neural Network (CNN), Long Short-Term Memory network (LSTM) and Deep Q-Network (DQN). A trust-based selector first picks the most reliable model in real time; a further model-fusion step then re-weights the outputs of all models to push accuracy beyond the best single model. Experiments on the “Miao-Suan” tactical wargame show that the framework raises average accuracy from 95.47\% (best single model) to 99.82\% while cutting variance by two orders of magnitude, without extra training cost.",
keywords = "Algorithm pool, Model selection, Reinforcement learning",
author = "Zhikai Zhou and Hongbin Ma and Ying Jin and Yehao Fang and Haipeng Wang and Rufei Zhang",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.; 9th International Workshop on Advanced Computational Intelligence and Intelligent Informatics, IWACIII 2025 ; Conference date: 31-10-2025 Through 04-11-2025",
year = "2026",
doi = "10.1007/978-981-95-6733-1\_13",
language = "English",
isbn = "9789819567324",
series = "Communications in Computer and Information Science",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "157--169",
editor = "Hongbin Ma and Bin Xin and Qing Wang and Jinhua She",
booktitle = "Advanced Computational Intelligence and Intelligent Informatics - 9th International Workshop, IWACIII 2025, Proceedings",
address = "Germany",
}