Reinforcement learning(RL)has become a dominant decision-making paradigm and has achieved notable success in many real-world applications.Notably,deep neural networks play a crucial role in unlocking RL’s potential i...Reinforcement learning(RL)has become a dominant decision-making paradigm and has achieved notable success in many real-world applications.Notably,deep neural networks play a crucial role in unlocking RL’s potential in large-scale decision-making tasks.Inspired by current major success of Transformer in natural language processing and computer vision,numerous bottlenecks have been overcome by combining Transformer with RL for decision-making.This paper presents a multiangle systematic survey of various Transformer-based RL(TransRL)models applied in decision-making tasks,including basic models,advanced algorithms,representative implementation instances,typical applications,and known challenges.Our work aims to provide insights into problems that inherently arise with the current RL approaches,and examines how we can address them with better TransRL models.To our knowledge,we are the first to present a comprehensive review of the recent Transformer research developments in RL for decision-making.We hope that this survey provides a comprehensive review of TransRL models and inspires the RL community in its pursuit of future directions.To keep track of the rapid TransRL developments in the decision-making domains,we summarize the latest papers and their open-source implementations at https://github.com/williamyuanv0/Transformer-in-Reinforcement-Learning-for-Decision-Making-A-Survey.展开更多
针对指挥控制组织设计中决策实体的配置问题,提出了一种问题的配置模型及其求解方法。在分析传统决策实体配置模型不足的基础上,采用了作战任务执行时间来测度决策实体工作负载,建立了以全部决策实体工作负载的均方根(root mean square,...针对指挥控制组织设计中决策实体的配置问题,提出了一种问题的配置模型及其求解方法。在分析传统决策实体配置模型不足的基础上,采用了作战任务执行时间来测度决策实体工作负载,建立了以全部决策实体工作负载的均方根(root mean square,RMS)最小为目标函数的问题数学模型。提出了基于最小RMS合并规则的层次聚类方法的问题求解思路,给出了该方法的具体步骤和流程。最后结合联合作战仿真算例中一个任务-平台的调度方案,验证了所提方法的有效性和优越性。展开更多
基金Project supported by the National Natural Science Foundation of China(No.62376280)。
文摘Reinforcement learning(RL)has become a dominant decision-making paradigm and has achieved notable success in many real-world applications.Notably,deep neural networks play a crucial role in unlocking RL’s potential in large-scale decision-making tasks.Inspired by current major success of Transformer in natural language processing and computer vision,numerous bottlenecks have been overcome by combining Transformer with RL for decision-making.This paper presents a multiangle systematic survey of various Transformer-based RL(TransRL)models applied in decision-making tasks,including basic models,advanced algorithms,representative implementation instances,typical applications,and known challenges.Our work aims to provide insights into problems that inherently arise with the current RL approaches,and examines how we can address them with better TransRL models.To our knowledge,we are the first to present a comprehensive review of the recent Transformer research developments in RL for decision-making.We hope that this survey provides a comprehensive review of TransRL models and inspires the RL community in its pursuit of future directions.To keep track of the rapid TransRL developments in the decision-making domains,we summarize the latest papers and their open-source implementations at https://github.com/williamyuanv0/Transformer-in-Reinforcement-Learning-for-Decision-Making-A-Survey.
文摘针对指挥控制组织设计中决策实体的配置问题,提出了一种问题的配置模型及其求解方法。在分析传统决策实体配置模型不足的基础上,采用了作战任务执行时间来测度决策实体工作负载,建立了以全部决策实体工作负载的均方根(root mean square,RMS)最小为目标函数的问题数学模型。提出了基于最小RMS合并规则的层次聚类方法的问题求解思路,给出了该方法的具体步骤和流程。最后结合联合作战仿真算例中一个任务-平台的调度方案,验证了所提方法的有效性和优越性。
