Reinforcement Learning Agents

Reinforcement Learning (RL) is learning through directexperimentation. It does not assume the existence of a teacher thatprovides examples upon which learning of a task takes place. Instead, inRL experience is the only teacher. With historical roots on the study ofbiological conditioned reflexes, RL attracts the interest of Engineersand Computer Scientists because of its theoretical relevance andpotential applications in fields as diverse as Operational Research andIntelligent Robotics.Computationally, RL is intended to operate in a learning environmentcomposed by two subjects: the learner and a dynamic process. Atsuccessive time steps, the learner makes an observation of the processstate, selects an action and applies it back to the process. Its goal isto find out an action policy that controls the behavior of the dynamicprocess, guided by signals (reinforcements) that indicate how badly orwell it has been performing the required task. These signals are usuallyassociated to a dramatic condition – e.g., accomplishment of a subtask(reward) or complete failure (punishment), and the learner tries tooptimize its behavior by using a performance measure (a function of thereceived reinforcements). The crucial point is that in order to do that,the learner must evaluate the conditions (associations between observedstates and chosen actions) that led to rewards or punishments.Starting from basic concepts, this tutorial presents the many flavorsof RL algorithms, develops the corresponding mathematical tools, assesstheir practical limitations and discusses alternatives that have beenproposed for applying RL to realistic tasks.     

现代电梯群控系统与人工智能技术

介绍用模糊逻辑、专家系统和神经网络等人工智能技术描述电梯交通系统的动态特性，讨论了人工智能技术在现代电梯群控系统中的应用。     

Robot Control Optimization Using Reinforcement Learning

Conventional robot control schemes are basically model-based methods. However, exact modeling of robot dynamics poses considerable problems and faces various uncertainties in task execution. This paper proposes a reinforcement learning control approach for overcoming such drawbacks. An artificial neural network (ANN) serves as the learning structure, and an applied stochastic real-valued (SRV) unit as the learning method. Initially, force tracking control of a two-link robot arm is simulated to verify the control design. The simulation results confirm that even without information related to the robot dynamic model and environment states, operation rules for simultaneous controlling force and velocity are achievable by repetitive exploration. Hitherto, however, an acceptable performance has demanded many learning iterations and the learning speed proved too slow for practical applications. The approach herein, therefore, improves the tracking performance by combining a conventional controller with a reinforcement learning strategy. Experimental results demonstrate improved trajectory tracking performance of a two-link direct-drive robot manipulator using the proposed method.     

基于模糊控制的智能小区电梯群控系统算法研究

研究并设计了一种基于模糊控制的多目标优化群控算法。首先对智能小区的四种交通模式进行了有效识别,然后对厅呼信号控制进行了重点研究。考虑到交通模式对三个评价指标（平均候梯时间、平均乘梯时间和能源消耗）权重的影响,本文采用综合量化评价的方法设计控制算法。通过控制相应的三个程度函数,达到乘客满意、系统节能的目的。     

智能多模式电梯群控调度方法的研究与实现

电梯群控系统的任务是有效地运送乘客,提高电梯运行效率,改善服务质量,根据大楼不同交通流状况识别不同的交通流模式,并采用最合适的调度方法分派电梯是提高群控性能的关键,实现了一种基于模式识别的智能多模式电梯群控调度方法,该方法可以在一天中根据不同的交通流状况,提供不同的群控策略,从而使电梯服务更优,仿真实验表明了这种电梯调度方法是有效的。     

新型电梯群控系统的建模和控制策略研究

针对电梯群控系统所要解决的是一个复杂的、具有非线性、不确定性的多目标随机系统的决策问题引入了细胞自动机理论建立电梯群控制系统的模型,模糊控制算法实现群控策略。该群控系统目前已经在实际运行,测试结果证明其运行可靠,性能稳定,有很好的实用性和推广价值。     

一种基于LonWorks的电梯群控系统实现方法

根据电梯群控系统的特性,设计了系统的调度软件,使用统计的方法判断群控系统的客流交通模式,确定了电梯目标评价函数,同时介绍了系统的控制模块设计方法。采用LonWorks现场总线技术,增强了系统的实时性和可靠性,并使其具有良好的可扩展性和推广价值。     

基于人工神经网络的自适应模糊电梯群控系统

针对电梯客流的随机性、离散性及电梯群控系统智能调度的复杂性。综合考虑了电梯运行的评价标准,结合了人工神经网络和模糊控制各自的优点,用神经网络的学习机制为模糊控制器自动提取并调整模糊规则及模糊隶属函数。提高了电梯群控的智能性。合理分配电梯应答,防止聚堆和忙闲不均情况的发生。大大减少了平均候梯时间和长候梯率。仿真实验及初步应用结果表明这种电梯调度方法是有效的。     

基于CC-Link现场总线的电梯群控系统

电梯群控系统是一个分布式控制系统,对稳定性和抗干扰性要求很高,传统的计算机通讯标准已不能满足可靠性和实时高速的通讯要求。针对这一问题,采用开放型的CC-Link现场总线来构建电梯群控网络,实现对每部电梯的局部控制和整体调度。在分析电梯工作原理的基础上,对电梯群控系统进行了设计,详细阐述了系统的硬件设计、通讯软件结构、PLC的地址分配和软件设计。应用表明,由CC—Link现场总线组成的网络控制系统．提高了系统的可靠性,实现了电梯系统的最优控制和管理．     

基于Canbus的电梯群控系统的设计

针对电梯和微机之间实时高速通讯的特点,结合现场总线技术尤其是CAN总线技术在国内的推广应用,完成了控制系统的设计。该系统是基于微机作为上位监控计算机,Pc1-841CAN卡等作为下位机的遵循CAN通信规程的通信系统。对系统的硬件结构和软件设计进行了深入的研究和系统的阐述,提出了实现方案。