dynamic programming

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Model-Based Value Iteration Algorithm for Deterministic Cleaning Robots A Reinforcement Learning and Dynamic Programming Example in MATLAB
Model-based value iteration algorithm for deterministic cleaning robots. This simple implementation of the value iteration algorithm serves as a helpful starting point for beginners in reinforcement learning and dynamic programming. The deterministic cleaning robot MDP involves the robot collecting used cans and recharging its battery. The state represents the robot's position, and the action defines the movement direction, either left or right. The first (1) and last (6) states are terminal states. The goal is to find the optimal policy to maximize the reward from any initial state. This is an example of Q-iteration (model-based value iteration DP). Reference: Algorithm 2-1, from: @book{busoniu2010reinforcement, title={Reinforcement Learning and Dynamic Programming Using Function Approximation}, authors={Busoniu, Lucian and Babuska, Robert and De Schutter, Bart and Ernst, Damien}, year={2010}, publisher={CRC Press}}.
Differential Scheme for Solving Problem 7A Dynamic Scripting Approach in Java-based Groovy Programming(2nd Edition)
The following differential schemes are used to solve the given problem: First Scheme: Combining equations (18), (21), and (22), we derive a differential scheme for solving problem (7). The general form is given as follows:[u^{n+1} = u^n + \Delta t \left( -\left( \frac{d^2 u}{dx^2} \right)^n \right)]In this case, the value of ( u_0 ) at layer ( j=0 ) is known. Using the above scheme, we can compute approximate values for nodes in the first layer ( j=1 ), then continue calculating successively for each layer. Classical Implicit Scheme: Rearranging equation (19) and combining with equations (21) and (22), we arrive at the following implicit differential scheme:[u^{n+1} = u^n + \Delta t \left( -\left( \frac{d^2 u}{dx^2} \right)^n + f(x, t) \right)]In this implicit scheme, though ( u_0 ) at the 0th layer is known, the calculation of values for subsequent layers ( j \geq 1 ) cannot be done directly. Hence, this scheme is termed the classical implicit format. Dufort-Frankel Scheme: The Dufort-Frankel scheme is a three-layer explicit scheme derived by combining equations (24), (25), and (26). Its specific form is:[u^{n+1} = u^n + \Delta t \left( \frac{1}{2} \left( \frac{d^2 u}{dx^2} \right)^n \right)]In this scheme, the value ( u_0 ) at the 0th layer is determined by the initial condition, and then values for subsequent layers are computed iteratively, starting from the 1st layer using a two-layer format. Hyperbolic Equation and Differential Solutions: For the second-order wave equation:[\frac{\partial^2 u}{\partial x^2} = a^2 \frac{\partial^2 u}{\partial t^2}]we define:[v = \frac{\partial u}{\partial x}, \quad \frac{\partial v}{\partial t} = \frac{\partial u}{\partial t}]This transforms the equation into a first-order linear system of hyperbolic equations."
Mastering Concurrent Programming with Scala
Scala并发编程学习指南 一、并发编程的重要性与挑战 随着计算机硬件技术的发展,多核处理器已经成为标准配置,这使得并发编程成为现代软件开发中不可或缺的一部分。并发编程利用多核处理器的能力来提高程序的执行效率和响应能力。然而,实现有效的并发编程并不简单,它涉及到对线程管理、数据共享、同步机制等方面的深入理解。 二、Scala语言在并发编程中的优势 Scala是一种多范式编程语言,结合了面向对象和函数式编程的特点。在并发编程领域,Scala提供了一系列高级工具和技术,使其成为处理复杂并发问题的理想选择: Actor模型:Scala内置了Actor模型支持,这是一种轻量级的消息传递系统,能够高效地管理并发任务。 Future和Promise:这些API简化了异步编程,使得编写非阻塞代码变得更加简单。 Reactive Streams:Scala支持Reactive Streams规范,用于构建高性能的流处理应用。 并行集合:Scala提供了并行集合,可以在多核处理器上自动并行化数据处理任务。 三、学习目标与内容概述 《Learning Concurrent Programming in Scala》第二版是一部深入解析如何使用Scala构建复杂、可扩展的并发应用程序的书籍。本书通过实际案例和理论讲解结合的方式,帮助读者掌握以下关键概念和技术: 并发基础:介绍并发的基本概念,包括线程、进程、并发与并行的区别等。 Scala并发模型:深入探讨Scala提供的各种并发机制,如Futures、Promises、Actors等,并解释它们的工作原理及应用场景。 并发编程模式:学习不同的并发编程模式,比如共享内存模型和消息传递模型,并比较它们的优缺点。 并发错误处理:讨论并发编程中常见的问题,如死锁、竞态条件等,并提供解决方案。 高级主题:涵盖高级主题,例如分布式计算、容错机制等。 四、并发编程实践案例 本书通过多个实际项目来展示并发编程的最佳实践。例如,涉及如何使用Actor模型设计一个简单的聊天服务器;或如何利用Futures和Promises构建一个高并发的Web爬虫。 五、并发编程工具与框架 在Scala的并发编程学习中,一些工具与框架也值得关注,它们进一步丰富了Scala的并发处理能力。
使用Programming Entity Framework DbContext
DbContext API涵盖了Entity Framework(EF)的大多数常用功能和任务,简化了EF的开发过程。这本简明的书向您展示了如何使用API来使用DbSet类执行集合操作,使用Change Tracker API处理变更跟踪和解决并发冲突,以及使用Validation API验证数据的更改。通过DbContext,您可以查询和更新数据,无论是处理单个对象还是对象图及其相关数据。书中包含大量的C#代码示例,帮助您快速入门。您只需要具备Visual Studio和数据库管理基础。
Elementary Number Theory and Programming Integration
Bridging an existing gap between mathematics and programming, Elementary Number Theory with Programming provides a unique introduction to elementary number theory with fundamental coverage of computer programming. Written by highly-qualified experts in the fields of computer science and mathematics, the book features accessible coverage for readers with various levels of experience and explores number theory in the context of programming without relying on advanced prerequisite knowledge and concepts in either area. Elementary Number Theory with Programming features comprehensive coverage of the methodology and applications of the most well-known theorems, problems, and concepts in number theory. Using standard mathematical applications within the programming field, the book presents modular arithmetic and prime decomposition, which are the basis of the public-private key system of cryptography.
Advanced Oracle SQL Programming Techniques
ORACLE SQL高级编程适合高级编程思想与原理,数据库权威教材。
IBM Informix Dynamic Server入门指南
这是一本Informix数据库入门指南的PDF文件,内容全面,适合初学者学习和参考。
Dynamic Template Matching with Variable Scale in MATLAB
Simple Template Matching with Variable Image Template Ratio: In this process, we aim to locate the template Itm (binary image) within the Is (grayscale image) using a canny edge-detected version of Is. The template matching method accommodates scaling, meaning the template size doesn't have to match the exact target size in the main image, allowing for scanning at various scale ratios of Itm. Input:- Is: The main color image containing the target.- Itm: The binary image of the template. Output:- Best Match Location (x, y): Coordinates of the optimal match.- Matching Score: Quality score of the best match.- Resized Image and Template: Outputs the resized main image and template for the best scale ratio found. Methodology:1. Edge Detection: Convert Is to a canny edge-detected image.2. Scale Adjustment: Iterate through various size scales of Itm and Is.3. Template Matching with Cross-Correlation: Utilize a cross-correlation approach to match edges between Is and Itm.4. Result Visualization: Display Is with the optimal match position highlighted. Primary Function: MAIN_find_template_in_image
Mastering Assembly Language Programming A Comprehensive Guide
The Art of Assembly Language ProgrammingVisitor Count: As of October 17, 1996 Forward Why Would Anyone Learn This Stuff? Chapter Overview: What’s Wrong With Assembly Language? - Examining criticisms and limitations. What’s Right With Assembly Language? - Understanding the benefits and uses. Text Organization and Teaching Approach - Overview of pedagogical concerns and text layout. Section One: Machine Organization Chapter 1 - Data Representation 1.1 Numbering Systems 1.1.1 - Overview of the Decimal System. 1.1.2 - Introduction to the Binary System. 1.1.3 - Binary Formats. 1.2 Data Organization 1.2.1 - Bits 1.2.2 - Nibbles 1.2.3 - Bytes 1.2.4 - Words 1.2.5 - Double Words 1.3 Hexadecimal Numbering System - Exploring hexadecimal representations. Arithmetic and Logical Operations 1.4 - Arithmetic on Binary and Hexadecimal. 1.5 - Logical Operations on Bits. 1.6 - Logical Operations on Binary Numbers and Bit Strings. Additional Concepts in Binary Data 1.7 Signed and Unsigned Numbers - Differences and implications. 1.8 Sign and Zero Extension - Practical uses. 1.9 Shifts and Rotates - Binary manipulation techniques. 1.10 Bit Fields and Packed Data - Efficient data storage methods. 1.11 The ASCII Character Set - Text representation in binary. Summary Assembly Language provides a foundational understanding of data representation, bitwise operations, and direct memory manipulation, essential for optimizing code and achieving hardware-level control.
Array Signal Applications A MATLAB Programming Guide
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