Transformer-Aided Deep Double Dueling Spatial-Temporal Q-Network for Spatial Crowdsourcing Analysis

With the rapid development ofmobile Internet,spatial crowdsourcing has becomemore andmore popular.Spatial crowdsourcing consists of many different types of applications,such as spatial crowd-sensing services.In terms of spatial crowd-sensing,it collects and analyzes traffic sensing data from clients like vehicles and traffic lights to construct intelligent traffic prediction models.Besides collecting sensing data,spatial crowdsourcing also includes spatial delivery services like DiDi and Uber.Appropriate task assignment and worker selection dominate the service quality for spatial crowdsourcing applications.Previous research conducted task assignments via traditional matching approaches or using simple network models.However,advanced mining methods are lacking to explore the relationship between workers,task publishers,and the spatio-temporal attributes in tasks.Therefore,in this paper,we propose a Deep Double Dueling Spatial-temporal Q Network(D3SQN)to adaptively learn the spatialtemporal relationship between task,task publishers,and workers in a dynamic environment to achieve optimal allocation.Specifically,D3SQNis revised through reinforcement learning by adding a spatial-temporal transformer that can estimate the expected state values and action advantages so as to improve the accuracy of task assignments.Extensive experiments are conducted over real data collected fromDiDi and ELM,and the simulation results verify the effectiveness of our proposed models.

基于知识融合和深度强化学习的智能紧急切机决策

紧急控制是在严重故障后维持电力系统暂态安全稳定的重要手段。目前常用的“人在环路”离线紧急控制决策制定方式存在效率不高、严重依赖专家经验等问题,该文提出一种基于知识融合和深度强化学习(deep reinforcement learning,DRL)的智能紧急切机决策制定方法。首先,构建基于DRL的紧急切机决策制定框架。然后,在智能体处理多个发电机决策时,由于产生的高维决策空间使得智能体训练困难,提出决策空间压缩和应用分支竞争Q(branching dueling Q,BDQ)网络的两种解决方法。接着,为了进一步提高智能体的探索效率和决策质量,在智能体训练中融合紧急切机控制相关知识经验。最后,在10机39节点系统中的仿真结果表明,所提方法可以在多发电机决策时快速给出有效的紧急切机决策,应用BDQ网络比决策空间压缩的决策性能更好,知识融合策略可引导智能体减少无效决策探索从而提升决策性能。

基于双智能体深度强化学习的交直流配电网经济调度方法

随着大量直流电源和负荷的接入,交直流混合的配电网技术已成为未来配电网的发展趋势.然而,源荷不确定性及可调度设备的类型多样化给配电网调度带来了巨大的挑战.本文提出了基于分支决斗深度强化网络(branching dueling Q-network,BDQ)和软演员-评论家(soft actor critic,SAC)双智能体深度强化学习的交直流配电网调度方法.该方法首先将经济调度问题与两智能体的动作、奖励、状态相结合,建立经济调度的马尔可夫决策过程,并分别基于BDQ和SAC方法设置两个智能体,其中,BDQ智能体用于控制配电网中离散动作设备,SAC智能体用于控制连续动作设备.然后,通过集中训练分散执行的方式,两智能体与环境进行交互,进行离线训练.最后,固定智能体的参数,进行在线调度.该方法的优势在于采用双智能体能够同时控制离散动作设备电容器组、载调压变压器和连续动作设备变流器、储能,同时通过对双智能体的集中训练,可以自适应源荷的不确定性.改进的IEEE33节点交直流配电网算例测试验证了所提方法的有效性.

A deep reinforcement learning method for multi-stage equipment development planning in uncertain environments

Equipment development planning(EDP)is usually a long-term process often performed in an environment with high uncertainty.The traditional multi-stage dynamic programming cannot cope with this kind of uncertainty with unpredictable situations.To deal with this problem,a multi-stage EDP model based on a deep reinforcement learning(DRL)algorithm is proposed to respond quickly to any environmental changes within a reasonable range.Firstly,the basic problem of multi-stage EDP is described,and a mathematical planning model is constructed.Then,for two kinds of uncertainties(future capabi lity requirements and the amount of investment in each stage),a corresponding DRL framework is designed to define the environment,state,action,and reward function for multi-stage EDP.After that,the dueling deep Q-network(Dueling DQN)algorithm is used to solve the multi-stage EDP to generate an approximately optimal multi-stage equipment development scheme.Finally,a case of ten kinds of equipment in 100 possible environments,which are randomly generated,is used to test the feasibility and effectiveness of the proposed models.The results show that the algorithm can respond instantaneously in any state of the multistage EDP environment and unlike traditional algorithms,the algorithm does not need to re-optimize the problem for any change in the environment.In addition,the algorithm can flexibly adjust at subsequent planning stages in the event of a change to the equipment capability requirements to adapt to the new requirements.