Model-Based Value Iteration Algorithm for Deterministic Cleaning Robots A Reinforcement Learning and Dynamic Programming Example in MATLAB

模拟退火算法模型实例，基于MATLAB的模拟退火算法说明解释及介绍。

基于系统均衡理论的浮动式停车计费模型 摘要与背景 探讨了一种基于系统均衡理论的浮动式停车计费模型。随着城市化进程的加快，停车难问题日益突出，如何合理配置有限的停车资源成为城市管理中的一个重要课题。现有的固定停车费率往往无法有效调节车辆在不同时间和地点的分布，导致部分区域停车资源过度拥挤而其他区域则资源闲置。因此，研究一种能够根据实时需求变化调整的停车计费策略至关重要。 停车选择行为模型 首先基于效用理论和非集计模型建立了一个停车选择行为模型。该模型考虑了停车费用、距离目的地远近、停车便利性等因素对驾驶员停车决策的影响。通过数学建模确定了停车费率等变量与选择概率之间的函数关系，从而量化了这些因素对停车选择行为的具体影响程度。 浮动式停车计费模型 在此基础上，构建了一个以出行者总停车选择效用最大化为目标的浮动式停车计费模型。该模型的核心在于通过动态调整停车费率来实现路网流量分配的均衡和停车场利用率的均衡。具体而言，模型将停车场利用率均衡与道路饱和度均衡作为约束条件，并采用序列二次规划方法进行求解。 实证分析 为了验证所提出的浮动式停车计费模型的有效性和可行性，研究人员通过算例进行了实证分析。结果显示，在采用浮动式停车费率的情况下，路网的流量分配更加均衡，停车场的利用率也得到了显著提高。相比于传统的固定费率策略，这种动态计费方式可以提升138%的停车社会效益。此外，研究还发现，浮动式停车费率对于停车系统的调控作用优于对道路系统的调控。 关键技术点解析 效用理论与非集计模型：效用理论用于衡量人们对某种商品或服务的偏好程度。在中，效用理论被用来评估驾驶员对于不同停车场的选择偏好。 系统均衡理论：在一个复杂的系统中寻找一种状态，使得系统内的各个组成部分都处于一个稳定的状态。 序列二次规划方法：主要用于解决具有连续变量的非线性优化问题。 交通均衡：在交通网络中寻求一种状态，使得所有出行者的效用最大化。

蚁群算法三臂凿岩机器人动态孔序规划。0积分下载，代码运行效果图见压缩包。

Big data technology has become a hot research topic in the field of education, focusing on analyzing large amounts of educational data collected to improve teaching methods and enhance education quality. Among educational big data, learning analytics is particularly important, as it helps teachers understand students' learning progress and implement personalized teaching, thus promoting teaching reform. In higher education, the application of big data-based learning analytics technology can monitor students' learning processes. By analyzing students' behavioral patterns during the learning process, teachers can gain a more intuitive understanding of each student's performance. This technology provides a series of insights such as 'who is learning', 'what is being learned', and 'how well students are learning', which is crucial for ensuring educational quality. Data collection is the first step in big data learning analytics, which involves utilizing various technical means to gather data from different sources. In the context of online education, the primary source of data is students' online behavior during the learning process. This data includes but is not limited to, video viewing patterns, discussion board participation scores, assignment scores, exam results, and forum interaction scores. These data need to be collected using appropriate tools such as web crawlers written in Python or by calling data through API interfaces. Once the data is collected, the next step is data preprocessing. This stage involves cleaning the data, removing unreliable data points like test accounts and extreme outliers. The goal of preprocessing is to ensure the accuracy of subsequent analysis, structure the data for easy storage, and prepare it for analysis. Data analysis is the core part of learning analytics and primarily includes statistical analysis and visualization, clustering analysis, predictive analytics, association rule mining, and text mining. These methods help teachers gain deeper insights into students' behavioral patterns, learning habits, and performance trends. Statistical analysis and visualization transform data into charts and graphs for intuitive representation of students' learning progress. Clustering analysis groups students by learning habits or grades, while predictive analytics forecasts students' future performance based on historical data. Association rule mining focuses on identifying relationships between students' behaviors, and text mining analyzes content from discussion boards to understand students' learning attitudes and thought processes. The application and development of big data in education holds great potential. With the rapid growth of global data, educational big data is gradually becoming a field of focus both domestically and internationally, offering significant value in education. In practical projects, the application of learning analytics has already shown results. For example, a research project mentioned in the article uses the 'C Programming 1' course on a MOOC platform to analyze students' learning behavior data combined with performance data to help teachers better understand students' progress and offer reasonable teaching suggestions. The application of big data in education, particularly in learning analytics on MOOC platforms, is becoming a key driver of educational reform.

在本项目中，我探索了在物理机器人上实现强化学习（RL）算法的过程，具体是在定制的3D打印机器人Benny和Bunny上从A到B的路径规划。作为我本科最后一年自选选修课的一部分，项目学习强化学习的基础知识。最初，编码直接在物理机器人上进行，但随着项目进展，意识到需要将算法与硬件解耦。仿真测试表明，在较小的状态空间（<= 100个状态）中表现良好，但在扩展到包含400个状态时，任何探索的RL算法均无法收敛。结果显示，在实现硬件前，需在仿真中探索更强大的算法。所有模拟代码均使用C++编写，确保代码的可移植性，以适应微控制器的限制，避免数据传输带来的复杂性。

This MATLAB example demonstrates the use of gradient descent to iteratively solve for the coefficients of a noisy quadratic curve. The algorithm is applied to fit a quadratic curve model, and the noisy data points are used to estimate the optimal coefficients through gradient descent optimization. This example is designed to inspire and help others understand how gradient descent can be applied in real-world curve fitting problems.

This article explores a new type of genetic algorithm in MATLAB that incorporates interactive learning. This innovative genetic algorithm technique aims to enhance the standard genetic algorithm by allowing solutions to learn from each other during the evolutionary process, thus improving overall performance and convergence speed. Key Features of the New Genetic Algorithm Interactive Learning Mechanism: Solutions exchange information during iterations, allowing for mutual learning, which enhances diversity and prevents premature convergence. Performance Optimization: Compared to traditional genetic algorithms, the introduction of an interactive component enables faster convergence and better optimization results. Application in MATLAB: The implementation of this genetic algorithm in MATLAB leverages the platform’s powerful computation capabilities, making it suitable for complex optimization tasks. Practical Applications The new genetic algorithm with interactive learning can be applied to various fields, including engineering design, machine learning, and data science, where optimization problems are prevalent. MATLAB’s rich toolset allows for seamless integration and testing of this algorithm across these domains. Code Example Below is a simple example to demonstrate the basic structure of this enhanced genetic algorithm in MATLAB: % Example of Enhanced Genetic Algorithm with Interactive Learning function optimized_solution = enhanced_genetic_algorithm(pop_size, generations) % Initialization population = initialize_population(pop_size); for gen = 1:generations % Evaluation and Selection fitness = evaluate_population(population); selected_parents = selection(population, fitness); % Crossover with Interactive Learning offspring = crossover_with_learning(selected_parents); % Mutation population = mutate(offspring); end optimized_solution = find_best_solution(population); end This function highlights the core stages: initialization, selection, crossover with learning, and mutation. Each step is designed to reinforce the algorithm's interactive learning framework.

Matlab光照模型代码欢迎使用Exemplar-SVM库，这是由卡内基梅隆大学开发的大型对象识别库，同时获得了我的机器人学博士学位。 - 托马斯·马里西维奇（Tomasz Malisiewicz）该代码是用Matlab编写的，是以下两个项目以及我的博士论文的基础：示例SVM的集成，用于对象检测及其他。在ICCV中，2011年。摘要提出了一种概念上简单但令人惊讶的强大方法，该方法将判别目标检测器的有效性与最近邻方法提供的显式对应相结合。该方法基于为训练集中的每个示例训练单独的线性SVM分类器。因此，这些示例SVM中的每一个都由一个正实例和数百万个负实例定义。尽管每个检测器对其示例都是非常特定的，但我们从经验上观察到，这样的示例SVM的集成提供了令人惊讶的良好通用性。我们在PASCAL VOC检测任务上的性能与Felzenszwalb等人的基于复杂潜在零件的模型相当复杂，只是计算成本有所增加。但是，我们方法的主要好处是，它在每次检测和单个训练样本之间建立了明确的关联。由于大多数检测都显示出与其相关样本的良好对齐，因此可以将任何可用的样本元数据（细分，几何）纳入考虑。

一种基于梯度的ICA算法 本算法利用梯度优化方法来实现独立成分分析(ICA)。ICA是一种常用于信号分离的技术，而梯度优化可以有效地提升算法的收敛速度和性能。以下是该算法的主要步骤： 初始化：设定初始的权重矩阵和学习率。 梯度计算：通过计算梯度，更新权重矩阵以最大化独立性。 收敛判定：当权重矩阵变化小于预定阈值时，判定收敛，输出分离信号。 优化更新：利用梯度下降法持续优化结果，确保分离效果的最优化。 该算法能够有效处理盲源分离问题，且具有较强的实际应用价值。