检索结果-内蒙古大学图书馆

您好，读者！请登录

内蒙古大学图书馆

首页
概况
党建
资源
服务
科研支持
- 论文收录引用证明
- 科技查新
知识产权
档案馆
帮助

咨询与建议

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

您的常用邮箱：*

您的手机号码：*

问题描述：

当前已输入0个字，您还可以输入200个字

全部搜索
期刊论文
图书
学位论文
标准
纸本馆藏
外文资源发现
数据库导航
超星发现

高级检索

时间限定

出版年份：

文献类型

图书期刊文献学位论文多媒体

馆藏选择

电子馆藏纸本馆藏

核心期刊

全部期刊 SCI 收录期刊 SSCI 收录期刊 EI 收录期刊 CSCD 收录期刊 CSSCI 收录期刊

语言

中文英文

文献类型

期刊文献图书学位论文标准纸本馆藏

帮助

文字说明：

T=题名（书名、题名），A=作者（责任者），K=主题词，P=出版物名称，PU=出版社名称，O=机构（作者单位、学位授予单位、专利申请人），L=中图分类号，C=学科分类号，U=全部字段，Y=年（出版发行年、学位年度、标准发布年）

检索规则说明：

AND代表“并且”；OR代表“或者”；NOT代表“不包含”；(注意必须大写,运算符两边需空一格)

检索范例：

范例一：(K=图书馆学 OR K=情报学) AND A=范并思 AND Y=1982-2016
范例二：P=计算机应用与软件 AND (U=C++ OR U=Basic) NOT K=Visual AND Y=2011-2016

分类表

所选分类

>> <<

限定检索结果

文献类型

299 篇 会议
8 篇 期刊文献

馆藏范围

307 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

180 篇 工学
- 158 篇 计算机科学与技术...
- 56 篇 电气工程
- 48 篇 软件工程
- 47 篇 控制科学与工程
- 13 篇 信息与通信工程
- 10 篇 机械工程
- 6 篇 仪器科学与技术
- 4 篇 力学（可授工学、理...
- 4 篇 生物工程
- 3 篇 动力工程及工程热...
- 2 篇 交通运输工程
- 2 篇 核科学与技术
- 2 篇 生物医学工程（可授...
- 1 篇 建筑学
- 1 篇 化学工程与技术
- 1 篇 航空宇航科学与技...
- 1 篇 食品科学与工程（可...
40 篇 理学
- 35 篇 数学
- 9 篇 系统科学
- 8 篇 统计学（可授理学、...
- 4 篇 物理学
- 4 篇 生物学
- 1 篇 化学
- 1 篇 天文学
- 1 篇 大气科学
- 1 篇 地球物理学
- 1 篇 地质学
18 篇 管理学
- 17 篇 管理科学与工程(可...
- 7 篇 工商管理
4 篇 经济学
- 4 篇 应用经济学
1 篇 医学

主题

115 篇 dynamic programm...
76 篇 reinforcement le...
67 篇 learning
47 篇 optimal control
30 篇 neural networks
27 篇 control systems
21 篇 approximate dyna...
21 篇 approximation al...
20 篇 function approxi...
20 篇 equations
17 篇 convergence
16 篇 adaptive dynamic...
16 篇 state-space meth...
16 篇 heuristic algori...
14 篇 mathematical mod...
13 篇 stochastic proce...
12 篇 learning (artifi...
12 篇 adaptive control
12 篇 cost function
11 篇 algorithm design...

机构

5 篇 arizona state un...
4 篇 department of el...
4 篇 school of inform...
4 篇 department of in...
4 篇 univ sci & techn...
4 篇 chinese acad sci...
4 篇 department of el...
3 篇 princeton univ d...
3 篇 northeastern uni...
3 篇 national science...
3 篇 robotics institu...
3 篇 univ illinois de...
3 篇 univ utrecht dep...
2 篇 univ groningen i...
2 篇 sharif univ tech...
2 篇 univ texas autom...
2 篇 pengcheng labora...
2 篇 guangxi univ sch...
2 篇 chinese acad sci...
2 篇 cemagref lisc au...

作者

14 篇 liu derong
9 篇 wei qinglai
8 篇 si jennie
7 篇 xu xin
5 篇 derong liu
4 篇 lewis frank l.
4 篇 martin riedmille...
4 篇 huaguang zhang
4 篇 jennie si
4 篇 marco a. wiering
4 篇 xin xu
4 篇 zhang huaguang
4 篇 dongbin zhao
4 篇 lei yang
4 篇 powell warren b.
4 篇 riedmiller marti...
3 篇 hado van hasselt
3 篇 van hasselt hado
3 篇 jagannathan s.
3 篇 munos remi

语言

305 篇 英文
1 篇 其他
1 篇 中文

检索条件"任意字段=IEEE International Symposium on Approximate Dynamic Programming and Reinforcement Learning"

共 307 条记录，以下是61-70 订阅

全选清除本页清除全部题录导出标记到"检索档案"

详细简洁

排序：

DHP adaptive critic motion control of autonomous wheeled mobile robot

DHP adaptive critic motion control of autonomous wheeled mob...

引用

ieee international symposium on approximate dynamic programming and reinforcement learning

作者： Lin, Wei-Song Yang, Ping-Chieh Natl Taiwan Univ Dept Elect Engn Inst Elect Engn 1 Sec 4Roosevelt Rd Taipei 106 Taiwan

ISBN: (纸本)9781424407064

Autonomous drive of wheeled mobile robot (WMR) needs implementing velocity and path tracking control subject to complex dynamical constraints. Conventionally, this control design is obtained by analysis and synthesis of the WMR system. This paper presents the dual heuristic programming (DHF) adaptive critic design of the motion control system that enables WMR to achieve the control purpose simply by learning through trial. The design consists of an adaptive critic velocity neuro-control loop and a posture neuro-control loop. The neural weights in the velocity neuro-controller (VNC) are corrected with the DHP adaptive critic method. The designer simply expresses the control objective with a utility function. The VNC learns by sequential optimization to satisfy the control objective. The posture neuro-controller (PNC) approximates the inverse velocity model of WMR so as to map planned positions to desired velocities. Supervised drive of WMR in variant velocities supplies training samples for the PNC and VNC to setup the neural weights. In autonomous drive, the learning mechanism keeps improving the PNC and VNC. The design is evaluated on an experimental WMR. The excellent results make it certain that the DHP adaptive critic motion control design enables WMR to develop the control ability autonomously.

关键词： adaptive critic design autonomous robot neuro-control dual heuristic programming reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

approximate dynamic programming for Stochastic Systems with Additive and Multiplicative Noise

Approximate Dynamic Programming for Stochastic Systems with ...

引用

ieee international symposium on Intelligent Control (ISIC)/ieee Multi-Conference on Systems and Control (MSC)

作者： Jiang, Yu Jiang, Zhong-Ping NYU Polytech Inst Dept Elect & Comp Engn Brooklyn NY 11201 USA

ISBN: (纸本)9781457711039

This paper studies the stochastic optimal control problem with additive and multiplicative noise via reinforcement learning (RL) and approximate/adaptive dynamic programming (ADP). Using Ito calculus, a policy iteration algorithm is derived in the presence of both additive and multiplicative noise. It is shown that the expectation of the approximated cost matrix is guaranteed to converge to the solution of certain algebraic Riccati equation that gives rise to the optimal cost value. Furthermore, the covariance of the approximated cost matrix can be reduced by increasing the length of time interval between two consecutive iterations. Finally, the efficiency of the proposed ADP methodology is illustrated in a numerical example.

关键词： Additives Approximation algorithms Convergence Covariance matrix Noise Steady-state Symmetric matrices

来源：评论

学校读者我要写书评

暂无评论

Adaptive sample collection using active learning for kernel-based approximate policy iteration

Adaptive sample collection using active learning for kernel-...

引用

ieee symposium on Adaptive dynamic programming and reinforcement learning

作者： Liu, Chunming Xu, Xin Haiyun Hu Dai, Bin College of Mechatronics and Automation National University of Defense Technology Changsha 410073 China

ISBN: (纸本)9781424498888

approximate policy iteration (API) has been shown to be a class of reinforcement learning methods with stability and sample efficiency. However, sample collection is still an open problem which is critical to the performance of API methods. In this paper, a novel adaptive sample collection strategy using active learning-based exploration is proposed to enhance the performance of kernel-based API. In this strategy, an online kernel-based least squares policy iteration (KLSPI) method is adopted to construct nonlinear features and approximate the Q-function simultaneously. Therefore, more representative samples can be obtained for value function approximation. Simulation results on typical learning control problems illustrate that by using the proposed strategy, the performance of KLSPI can be improved remarkably. © 2011 ieee.

关键词： reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

reinforcement-learning-based magneto-hydrodynamic control of hypersonic flows

Reinforcement-learning-based magneto-hydrodynamic control of...

引用

ieee international symposium on approximate dynamic programming and reinforcement learning

作者： Kulkarni, Nilesh V. Phan, Minh Q. NASA Ames Res Ctr QSS Grp Inc Moffett Field CA 94035 USA Dartmouth Coll Thayer Sch Engn Hanover NH 03755 USA

ISBN: (纸本)9781424407064

In this work, we design a policy-iteration-based Q-learning approach for on-line optimal control of ionized hypersonic flow at the inlet of a scramjet engine. Magneto-hydrodynamics (MHD) has been recently proposed as a means for flow control in various aerospace problems. This mechanism corresponds to applying external magnetic fields to ionized flows towards achieving desired flow behavior. The applications range from external flow control for producing forces and moments on the air-vehicle to internal flow control designs, which compress and extract electrical energy from the flow. The current work looks at the later problem of internal flow control. The baseline controller and Q-function parameterizations are derived from an off-line mixed predictive-control and dynamic-programming-based design. The nominal optimal neural network Q-function and controller are updated on-line to handle modeling errors in the off-line design. The on-line implementation investigates key concerns regarding the conservativeness of the update methods. Value-iteration-based update methods have been shown to converge in a probabilistic sense. However, simulations results illustrate that realistic implementations of these methods face significant training difficulties, often failing in learning the optimal controller on-line. The present approach, therefore, uses a policyiteration-based update, which has time-based convergence guarantees. Given the special finite-horizon nature of the problem, three novel on-line update algorithms are proposed. These algorithms incorporate different mix of concepts, which include bootstrapping, and forward and backward dynamic programming update rules. Simulation results illustrate success of the proposed update algorithms in re-optimizing the performance of the MHD generator during system operation.

关键词： reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

reinforcement learning in the Game of Othello: learning Against a Fixed Opponent and learning from Self-Play

Reinforcement Learning in the Game of Othello: Learning Agai...

引用

4th ieee international symposium on Adaptive dynamic programming and reinforcement learning (ADPRL)

作者： van der Ree, Michiel Wiering, Marco Univ Groningen Inst Artificial Intelligence & Cognit Engn Fac Math & Nat Sci NL-9700 AB Groningen Netherlands

ISBN: (纸本)9781467359252

This paper compares three strategies in using reinforcement learning algorithms to let an artificial agent learn to play the game of Othello. The three strategies that are compared are: learning by self-play, learning from playing against a fixed opponent, and learning from playing against a fixed opponent while learning from the opponent's moves as well. These issues are considered for the algorithms Q-learning, Sarsa and TD-learning. These three reinforcement learning algorithms are combined with multi-layer perceptrons and trained and tested against three fixed opponents. It is found that the best strategy of learning differs per algorithm. Q-learning and Sarsa perform best when trained against the fixed opponent they are also tested against, whereas TD-learning performs best when trained through self-play. Surprisingly, Q-learning and Sarsa outperform TD-learning against the stronger fixed opponents, when all methods use their best strategy. learning from the opponent's moves as well leads to worse results compared to learning only from the learning agent's own moves.

关键词： learning Game theory Game animals learning (artificial intelligence) Fixed Hunting

来源：评论

学校读者我要写书评

暂无评论

Adaptive dynamic programming-based optimal tracking control for nonlinear systems using general value iteration

Adaptive dynamic programming-based optimal tracking control ...

引用

ieee symposium on Adaptive dynamic programming and reinforcement learning (ADPRL)

作者： Lin, Xiaofeng Ding, Qiang Kong, Weikai Song, Chunning Huang, Qingbao Guangxi Univ Sch Elect Engn Nanning Peoples R China

ISBN: (纸本)9781479945528

For the optimal tracking control problem of affine nonlinear systems, a general value iteration algorithm based on adaptive dynamic programming is proposed in this paper. By system transformation, the optimal tracking problem is converted into the optimal regulating problem for the tracking error dynamics. Then, general value iteration algorithm is developed to obtain the optimal control with convergence analysis. Considering the advantages of echo state network, we use three echo state networks with levenberg-Marquardt (LM) adjusting algorithm to approximate the system, the cost function and the control law. A simulation example is given to demonstrate the effectiveness of the presented scheme.

关键词： Adaptive dynamic programming value iteration tracking control echo state network

来源：评论

学校读者我要写书评

暂无评论

A Combined Hierarchical reinforcement learning Based Approach For Multi-robot Cooperative Target Searching in Complex Unknown Environments

A Combined Hierarchical Reinforcement Learning Based Approac...

引用

4th ieee international symposium on Adaptive dynamic programming and reinforcement learning (ADPRL)

作者： Cai, Yifan Yang, Simon X. Xu, Xin Univ Guelph Sch Engn Guelph ON N1G 2W1 Canada Natl Univ Def Technol Coll Mechatron & Automat Changsha 410073 Hunan Peoples R China

ISBN: (纸本)9781467359252

Effective cooperation of multi-robots in unknown environments is essential in many robotic applications, such as environment exploration and target searching. In this paper, a combined hierarchical reinforcement learning approach, together with a designed cooperation strategy, is proposed for the real-time cooperation of multi-robots in completely unknown environments. Unlike other algorithms that need an explicit environment model or select parameters by trial and error, the proposed cooperation method obtains all the required parameters automatically through learning. By integrating segmental options with the traditional MAXQ algorithm, the cooperation hierarchy is built. In new tasks, the designed cooperation method can control the multi-robot system to complete the task effectively. The simulation results demonstrate that the proposed scheme is able to effectively and efficiently lead a team of robots to cooperatively accomplish target searching tasks in completely unknown environments.

关键词： Hierarchical reinforcement learning multi-robot cooperation complex unknown environment target searching

来源：评论

学校读者我要写书评

暂无评论

Finite Horizon Stochastic Optimal Control of Uncertain Linear Networked Control System

Finite Horizon Stochastic Optimal Control of Uncertain Linea...

引用

4th ieee international symposium on Adaptive dynamic programming and reinforcement learning (ADPRL)

作者： Xu, Hao Jagannathan, S. Missouri Univ Sci & Technol Dept Elect & Comp Engn Rolla MO 65409 USA

ISBN: (纸本)9781467359252

In this paper, finite horizon stochastic optimal control issue has been studied for linear networked control system (LNCS) in the presence of network imperfections such as network-induced delays and packet losses by using adaptive dynamic programming (ADP) approach. Due to an uncertainty in system dynamics resulting from network imperfections, the stochastic optimal control design uses a novel adaptive estimator (AE) to solve the optimal regulation of uncertain LNCS in a forward-in-time manner in contrast with backward-in-time Riccati equation-based optimal control with known system dynamics. Tuning law for unknown parameters of AE has been derived. Lyapunov theory is used to show that all the signals are uniformly ultimately bounded (UUB) with ultimate bounds being a function of initial values and final time. In addition, the estimated control input converges to optimal control input within finite horizon. Simulation results are included to show the effectiveness of the proposed scheme.

关键词： Networked Control System Adaptive dynamics programming and reinforcement learning Finite horizon Stochastic Optimal Control Adaptive Estimator

来源：评论

学校读者我要写书评

暂无评论

Iterative Local dynamic programming

Iterative Local Dynamic Programming

引用

ieee symposium on Adaptive dynamic programming and reinforcement learning

作者： Todorov, Emanuel Tassa, Yuval Univ Calif San Diego Dept Cognit Sci La Jolla CA 92093 USA Hebrew Univ Jerusalem Ctr Neural Computat IL-91905 Jerusalem Israel

ISBN: (纸本)9781424427611

We develop an iterative local dynamic programming method (iLDP) applicable to stochastic optimal control problems in continuous high-dimensional state and action spaces. Such problems are common in the control of biological movement, but cannot be handled by existing methods. iLDP can be considered a generalization of Differential dynamic programming, inasmuch as: (a) we use general basis functions rather than quadratics to approximate the optimal value function;(b) we introduce a collocation method that dispenses with explicit differentiation of the cost and dynamics and ties iLDP to the Unscented Kalman filter;(c) we adapt the local function approximator to the propagated state covariance, thus increasing accuracy at more likely states. Convergence is similar to quasi-Netwon methods. We illustrate iLDP on several problems including the "swimmer" dynamical system which has 14 state and 4 control variables.

关键词： dynamical systems

来源：评论

学校读者我要写书评

暂无评论

A scalable model-free recurrent neural network framework for solving POMDPs

A scalable model-free recurrent neural network framework for...

引用

ieee international symposium on approximate dynamic programming and reinforcement learning

作者： Liu, Zhenzhen Elhanany, Itamar Univ Tennessee Dept Elect & Comp Engn Knoxville TN 37996 USA

ISBN: (纸本)9781424407064

This paper presents a framework for obtaining an optimal policy in model-free Partially Observable Markov Decision Problems (POMDPs) using a recurrent neural network (RNN). A Q-function approximation approach is taken, utilizing a novel RNN architecture with computation and storage requirements that are dramatically reduced when compared to existing schemes. A scalable online training algorithm, derived from the real-time recurrent learning (RTRL) algorithm, is employed. Moreover, stochastic meta-descent (SMD), an adaptive step size scheme for stochastic gradient-descent problems, is utilized as means of incorporating curvature information to accelerate the learning process. We consider case studies of POMDPs where state information is not directly available to the agent. Particularly, we investigate scenarios in which the agent receives indentical observations for multiple states, thereby relying on temporal dependencies captured by the RNN to obtain the optimal policy. Simulation results illustrate the effectiveness of the approach along with substantial improvement in convergence rate when compared to existing schemes. Index Terms-Recurrent neural networks, real-time recurrent learning (RTRL), constraint optimization.

关键词： Constraint optimization Real-lime recurrent learning (RTRL) Recurrent neural networks

来源：评论

学校读者我要写书评

暂无评论

没有更多数据了...

全选清除本页清除全部题录导出标记到“检索档案”

共31页 << < 3 4 5 6 7 8 9 10 11 12 > >>

检索报告对象比较合并检索0

隐藏清空

合并搜索

回到顶部

执行限定条件

内容：

评分：

请选择保存的检索档案：

请选择收藏分类：

订阅名称：

通借通还

温馨提示：

图书名称：

借书校区：

取书校区：

手机号码：

邮箱地址：

一卡通帐号：

电话和邮箱必须正确填写，我们会与您联系确认。

联系人：

所在院系：

联系邮箱：

联系电话：

内蒙古自治区呼和浩特市赛罕区大学西街235号邮编: 010021

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：