To solve the problem of multi-target hunting by an unmanned surface vehicle(USV)fleet,a hunting algorithm based on multi-agent reinforcement learning is proposed.Firstly,the hunting environment and kinematic model wit...To solve the problem of multi-target hunting by an unmanned surface vehicle(USV)fleet,a hunting algorithm based on multi-agent reinforcement learning is proposed.Firstly,the hunting environment and kinematic model without boundary constraints are built,and the criteria for successful target capture are given.Then,the cooperative hunting problem of a USV fleet is modeled as a decentralized partially observable Markov decision process(Dec-POMDP),and a distributed partially observable multitarget hunting Proximal Policy Optimization(DPOMH-PPO)algorithm applicable to USVs is proposed.In addition,an observation model,a reward function and the action space applicable to multi-target hunting tasks are designed.To deal with the dynamic change of observational feature dimension input by partially observable systems,a feature embedding block is proposed.By combining the two feature compression methods of column-wise max pooling(CMP)and column-wise average-pooling(CAP),observational feature encoding is established.Finally,the centralized training and decentralized execution framework is adopted to complete the training of hunting strategy.Each USV in the fleet shares the same policy and perform actions independently.Simulation experiments have verified the effectiveness of the DPOMH-PPO algorithm in the test scenarios with different numbers of USVs.Moreover,the advantages of the proposed model are comprehensively analyzed from the aspects of algorithm performance,migration effect in task scenarios and self-organization capability after being damaged,the potential deployment and application of DPOMH-PPO in the real environment is verified.展开更多
阶梯式碳交易机制以及优化调度模型求解算法是进行园区综合能源系统(community integrated energy system,CIES)优化调度的重要因素,现有文献对这两个因素的考虑不够全面。为此,文中在考虑阶梯式碳交易机制的基础上,提出采用近端策略优...阶梯式碳交易机制以及优化调度模型求解算法是进行园区综合能源系统(community integrated energy system,CIES)优化调度的重要因素,现有文献对这两个因素的考虑不够全面。为此,文中在考虑阶梯式碳交易机制的基础上,提出采用近端策略优化(proximal policy optimization,PPO)算法求解CIES低碳优化调度问题。该方法基于低碳优化调度模型搭建强化学习交互环境,利用设备状态参数及运行参数定义智能体的状态、动作空间及奖励函数,再通过离线训练获取可生成最优策略的智能体。算例分析结果表明,采用PPO算法得到的CIES低碳优化调度方法能够充分发挥阶梯式碳交易机制减少碳排放量和提高能源利用率方面的优势。展开更多
随着空间目标的数量逐渐增多、空中目标动态性日趋提升,对目标的观测定位问题变得愈发重要.由于需同时观测的目标多且目标动态性强,而星座观测资源有限,为了更高效地调用星座观测资源,需要动态调整多目标协同观测方案,使各目标均具有较...随着空间目标的数量逐渐增多、空中目标动态性日趋提升,对目标的观测定位问题变得愈发重要.由于需同时观测的目标多且目标动态性强,而星座观测资源有限,为了更高效地调用星座观测资源,需要动态调整多目标协同观测方案,使各目标均具有较好的定位精度,因此需解决星座协同观测多目标的任务规划问题.建立星座姿态轨道模型、目标飞行模型、目标协同探测及定位模型,提出基于几何精度衰减因子(geometric dilution of precision, GDOP)的目标观测定位误差预估模型及目标观测优先级模型,建立基于强化学习的协同观测任务规划框架,采用多头自注意力机制建立策略网络,以及近端策略优化算法开展任务规划算法训练.仿真验证论文提出的方法相比传统启发式方法提升了多目标观测精度和有效跟踪时间,相比遗传算法具有更快的计算速度.展开更多
基金financial support from National Natural Science Foundation of China(Grant No.61601491)Natural Science Foundation of Hubei Province,China(Grant No.2018CFC865)Military Research Project of China(-Grant No.YJ2020B117)。
文摘To solve the problem of multi-target hunting by an unmanned surface vehicle(USV)fleet,a hunting algorithm based on multi-agent reinforcement learning is proposed.Firstly,the hunting environment and kinematic model without boundary constraints are built,and the criteria for successful target capture are given.Then,the cooperative hunting problem of a USV fleet is modeled as a decentralized partially observable Markov decision process(Dec-POMDP),and a distributed partially observable multitarget hunting Proximal Policy Optimization(DPOMH-PPO)algorithm applicable to USVs is proposed.In addition,an observation model,a reward function and the action space applicable to multi-target hunting tasks are designed.To deal with the dynamic change of observational feature dimension input by partially observable systems,a feature embedding block is proposed.By combining the two feature compression methods of column-wise max pooling(CMP)and column-wise average-pooling(CAP),observational feature encoding is established.Finally,the centralized training and decentralized execution framework is adopted to complete the training of hunting strategy.Each USV in the fleet shares the same policy and perform actions independently.Simulation experiments have verified the effectiveness of the DPOMH-PPO algorithm in the test scenarios with different numbers of USVs.Moreover,the advantages of the proposed model are comprehensively analyzed from the aspects of algorithm performance,migration effect in task scenarios and self-organization capability after being damaged,the potential deployment and application of DPOMH-PPO in the real environment is verified.
文摘随着空间目标的数量逐渐增多、空中目标动态性日趋提升,对目标的观测定位问题变得愈发重要.由于需同时观测的目标多且目标动态性强,而星座观测资源有限,为了更高效地调用星座观测资源,需要动态调整多目标协同观测方案,使各目标均具有较好的定位精度,因此需解决星座协同观测多目标的任务规划问题.建立星座姿态轨道模型、目标飞行模型、目标协同探测及定位模型,提出基于几何精度衰减因子(geometric dilution of precision, GDOP)的目标观测定位误差预估模型及目标观测优先级模型,建立基于强化学习的协同观测任务规划框架,采用多头自注意力机制建立策略网络,以及近端策略优化算法开展任务规划算法训练.仿真验证论文提出的方法相比传统启发式方法提升了多目标观测精度和有效跟踪时间,相比遗传算法具有更快的计算速度.
