value iteration

当前话题为您枚举了最新的 value iteration。在这里,您可以轻松访问广泛的教程、示例代码和实用工具,帮助您有效地学习和应用这些核心编程技术。查看页面下方的资源列表,快速下载您需要的资料。我们的资源覆盖从基础到高级的各种主题,无论您是初学者还是有经验的开发者,都能找到有价值的信息。

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}}.
Oracle 高级查询:分析函数 first_value 和 last_value
本指南介绍了 Oracle 高级查询中分析函数 first_value 和 last_value 的用法。这些函数用于获取数据组中第一行或最后一行中的值。
Matlab Singular Value Decomposition Solutions
很不错的Matlab代码,可以很好的解决奇异值分解问题。
Efficient Algorithms for Frequent Sequence Mining and Load Value Prediction
This research focuses on developing novel algorithms for two key areas: frequent sequence mining in transactional databases and enhanced load value prediction. A novel algorithm, SPAM (Sequential Pattern Mining Algorithm), is introduced to efficiently discover frequent sequences, even those of considerable length. SPAM leverages advanced pruning and indexing techniques to optimize its search. Furthermore, the research explores load value prediction (LVP) through identifying frequent patterns within program memory access traces. These discovered patterns serve as the foundation for developing efficient pre-fetching strategies, leading to improved performance.
key 和 value 值的存储位置
redisObject 抽象了要存储的不同类型对象(如字符串、列表、集合等)。type(长度为 4 比特整数)标识对象的类型,ptr 指向对象在内存中的地址。dictEntry 中只保存获取 key 和 value 值的指针,而 key 和 value 值本身存储在内存中,哈希表中只存储指向它们的指针。添加一个对象时,会分别为 key 和 value 分配内存,再将指向它们的指针存储到哈希表中。
Redis:一个高性能 Key-Value 数据库
Redis 是一个开源的内存数据库,提供了一个快速高效的 Key-Value 存储解决方案。它支持网络通信,具有持久化功能,并提供了多种语言的 API。
Redis入门到精通全面掌握Key-Value存储系统
Redis简介 Redis是一个高效的key-value存储系统,和Memcached类似,但支持的数据类型更加丰富。常用的数据类型包括: string(字符串) list(链表) set(集合) zset(有序集合) hash(哈希类型) 这些类型支持push/pop、add/remove以及取交集、并集、差集等多种操作,而且所有操作均具备原子性。基于这些特性,Redis支持灵活的数据排序。 Redis数据操作 每种数据类型在Redis中有独特的应用场景,以下是主要类型及其操作: String:简单的键值对,常用于缓存 List:支持队列操作(如FIFO、LIFO) Set:去重数据集,支持求交集、并集 Sorted Set:有序数据集合,按权重排序 Hash:适合存储对象的属性,如用户信息 Redis原子操作与排序 所有数据操作都是原子性的,即在并发操作下依然可以保持一致性。此外,Redis还提供多种排序方式,以满足复杂数据处理需求。 总结 Redis不仅仅是一个存储系统,它更是一个强大的数据管理工具,提供了丰富的数据操作和排序功能,适用于高性能需求的场景。
Redis-4.0.6开源高性能Key-Value数据库
Redis(Remote Dictionary Server),即远程字典服务,是一个开源的使用ANSI C语言编写、支持网络、可基于内存亦可持久化的日志型、Key-Value数据库。它提供了多种语言的API,支持高效的数据存储和访问,广泛应用于缓存、消息队列、实时数据分析等场景。
Simultaneous Heat Transfer Search Single-Objective Heat Transfer Search(Termination Criterion Iteration Count)-MATLAB Development
Simultaneous Heat Transfer Search (SHTS) is a single-objective optimization technique designed for unconstrained problems. Unlike traditional heat transfer search, which utilizes only one heat transfer mode per iteration, SHTS divides the population and simultaneously applies all three heat transfer modes. The main differences between SHTS and HTS can be found in the following references: Synchronous Heat Transfer Search for Costly Numerical Optimization, 2016 IEEE Congress on Evolutionary Computation (CEC), 2016, pp. 2982-2988; IEEE Paper Link Simultaneous Heat Transfer Search for Single-Objective Real Parameter Optimization Problems, TENCON 2016 IEEE, pp. 2138-2141, 2016; IEEE Paper Link Note: If N is the population size, exactly N function evaluations are required during a complete iteration of SHTS. For T iterations, the total number of function evaluations will be NT.
Apache Accumulo高性能分布式Key-Value存储解决方案
Accumulo是一种可靠、可伸缩、高性能的分布式Key-Value存储系统,基于Google BigTable设计,使用Apache Hadoop、Zookeeper和Thrift构建。它支持自动负载平衡、数据压缩和细粒度安全标签,适用于大规模数据存储和处理。