Scholarlink

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.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Online Model-Pool Selection and Fusion for Adaptive MARL in Wargames

  • 会议论文

  • 发表日期：

    2026年

查看全文
  • Zhikai Zhou,
  • Hongbin Ma,
  • Ying Jin,
  • Yehao Fang,
  • Haipeng Wang,
  • Rufei Zhang

摘要

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.