Multi-Agent Reinforcement Learning(MARL)has proven to be successful in cooperative assignments.MARL is used to investigate how autonomous agents with the same interests can connect and act in one team.MARL cooperation...Multi-Agent Reinforcement Learning(MARL)has proven to be successful in cooperative assignments.MARL is used to investigate how autonomous agents with the same interests can connect and act in one team.MARL cooperation scenarios are explored in recreational cooperative augmented reality environments,as well as realworld scenarios in robotics.In this paper,we explore the realm of MARL and its potential applications in cooperative assignments.Our focus is on developing a multi-agent system that can collaborate to attack or defend against enemies and achieve victory withminimal damage.To accomplish this,we utilize the StarCraftMulti-Agent Challenge(SMAC)environment and train four MARL algorithms:Q-learning with Mixtures of Experts(QMIX),Value-DecompositionNetwork(VDN),Multi-agent Proximal PolicyOptimizer(MAPPO),andMulti-Agent Actor Attention Critic(MAA2C).These algorithms allow multiple agents to cooperate in a specific scenario to achieve the targeted mission.Our results show that the QMIX algorithm outperforms the other three algorithms in the attacking scenario,while the VDN algorithm achieves the best results in the defending scenario.Specifically,the VDNalgorithmreaches the highest value of battle wonmean and the lowest value of dead alliesmean.Our research demonstrates the potential forMARL algorithms to be used in real-world applications,such as controllingmultiple robots to provide helpful services or coordinating teams of agents to accomplish tasks that would be impossible for a human to do.The SMAC environment provides a unique opportunity to test and evaluate MARL algorithms in a challenging and dynamic environment,and our results show that these algorithms can be used to achieve victory with minimal damage.展开更多
第二个线粒体衍生半胱天冬氨酸蛋白酶激活剂(second mitochondria‐derived activator of caspase,Smac)蛋白是一类存在于线粒体中的凋亡抑制蛋白(inhibitor of apoptosis protein,IAP)拮抗剂,在肿瘤细胞凋亡过程中从线粒体释放,与IAP...第二个线粒体衍生半胱天冬氨酸蛋白酶激活剂(second mitochondria‐derived activator of caspase,Smac)蛋白是一类存在于线粒体中的凋亡抑制蛋白(inhibitor of apoptosis protein,IAP)拮抗剂,在肿瘤细胞凋亡过程中从线粒体释放,与IAP结合抑制其功能。目前Smac模拟物已经成为癌症中一类很有前途的靶向疗法,正在进行临床测试。乳腺癌作为全球女性最常见的癌症,其治疗受到广泛关注。本文针对Smac模拟物在乳腺癌治疗中的相关研究进展作一综述。展开更多
The impact that the digital transformation(DT)has on businesses,suppliers,and other third parties has increased significantly now.Digital transformation means improving traditional manufacturing processes with the hel...The impact that the digital transformation(DT)has on businesses,suppliers,and other third parties has increased significantly now.Digital transformation means improving traditional manufacturing processes with the help of digital technologies.The goal of digital transformation is to increase production efficiency and reduce costs,improve the quality of goods and services produced,and quickly adapt to changes in the global market.The state of industrial production is constantly changing due to the instability of global,economic and political decisions,so the adoption and expansion of digital solutions based on Industry 4.0,the Internet of things,machine learning,and other technologies of the future is accelerating.With the help of these technologies,companies are trying to change approaches and find new ways to solve problems.In this article the author analyzed the phenomenon of a complex system of knowledge management with tools as SMAC,AI,IoT and Edge computing in intelligent organizations as a part of intelligent economy.The arguments are illustrated with the results of own research conducted by the author in 2021-2022 in selected SMEs from the Polish Wielkopolska Province and their reference to the general development trends in this area.展开更多
基金supported in part by United States Air Force Research Institute for Tactical Autonomy(RITA)University Affiliated Research Center(UARC)in part by the United States Air Force Office of Scientific Research(AFOSR)Contract FA9550-22-1-0268 awarded to KHA,https://www.afrl.af.mil/AFOSR/The contract is entitled:“Investigating Improving Safety of Autonomous Exploring Intelligent Agents with Human-in-the-Loop Reinforcement Learning,”and in part by Jackson State University.
文摘Multi-Agent Reinforcement Learning(MARL)has proven to be successful in cooperative assignments.MARL is used to investigate how autonomous agents with the same interests can connect and act in one team.MARL cooperation scenarios are explored in recreational cooperative augmented reality environments,as well as realworld scenarios in robotics.In this paper,we explore the realm of MARL and its potential applications in cooperative assignments.Our focus is on developing a multi-agent system that can collaborate to attack or defend against enemies and achieve victory withminimal damage.To accomplish this,we utilize the StarCraftMulti-Agent Challenge(SMAC)environment and train four MARL algorithms:Q-learning with Mixtures of Experts(QMIX),Value-DecompositionNetwork(VDN),Multi-agent Proximal PolicyOptimizer(MAPPO),andMulti-Agent Actor Attention Critic(MAA2C).These algorithms allow multiple agents to cooperate in a specific scenario to achieve the targeted mission.Our results show that the QMIX algorithm outperforms the other three algorithms in the attacking scenario,while the VDN algorithm achieves the best results in the defending scenario.Specifically,the VDNalgorithmreaches the highest value of battle wonmean and the lowest value of dead alliesmean.Our research demonstrates the potential forMARL algorithms to be used in real-world applications,such as controllingmultiple robots to provide helpful services or coordinating teams of agents to accomplish tasks that would be impossible for a human to do.The SMAC environment provides a unique opportunity to test and evaluate MARL algorithms in a challenging and dynamic environment,and our results show that these algorithms can be used to achieve victory with minimal damage.
文摘第二个线粒体衍生半胱天冬氨酸蛋白酶激活剂(second mitochondria‐derived activator of caspase,Smac)蛋白是一类存在于线粒体中的凋亡抑制蛋白(inhibitor of apoptosis protein,IAP)拮抗剂,在肿瘤细胞凋亡过程中从线粒体释放,与IAP结合抑制其功能。目前Smac模拟物已经成为癌症中一类很有前途的靶向疗法,正在进行临床测试。乳腺癌作为全球女性最常见的癌症,其治疗受到广泛关注。本文针对Smac模拟物在乳腺癌治疗中的相关研究进展作一综述。
文摘The impact that the digital transformation(DT)has on businesses,suppliers,and other third parties has increased significantly now.Digital transformation means improving traditional manufacturing processes with the help of digital technologies.The goal of digital transformation is to increase production efficiency and reduce costs,improve the quality of goods and services produced,and quickly adapt to changes in the global market.The state of industrial production is constantly changing due to the instability of global,economic and political decisions,so the adoption and expansion of digital solutions based on Industry 4.0,the Internet of things,machine learning,and other technologies of the future is accelerating.With the help of these technologies,companies are trying to change approaches and find new ways to solve problems.In this article the author analyzed the phenomenon of a complex system of knowledge management with tools as SMAC,AI,IoT and Edge computing in intelligent organizations as a part of intelligent economy.The arguments are illustrated with the results of own research conducted by the author in 2021-2022 in selected SMEs from the Polish Wielkopolska Province and their reference to the general development trends in this area.
