在 中文 中使用 动态规划 的示例及其翻译为 英语
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动态规划只能应用于有最优子结构的问题。
动态规划使我们能够只建立一次PPinmypajamas。
Dynamic programming allows us to build the PP in my pajamas just once.函数virahanka2()实现动态规划方法解决这个问题。
Function virahanka2() implements a dynamic programming approach to the problem.大多数动态规划问题都能被归类成两种类型：.
Majority of the Dynamic Programming problems can be categorized into two types.我们还将探索一些简单而有效的动态规划解决方案。
We will also explore some simple and effective dynamic programming solutions.Combinations with other parts of speech
如何解决动态规划问题？?
How to solve a Dynamic Programming Problem?下面是动态规划的程序：.
Below is the dynamic programming procedure.什么是动态规划，我们要如何描述它?
What is a dynamic programming, how can it be described?简介(入门)什么是动态规划，我们要如何描述它?
What is a dynamic programming, how can it be described?就数学优化（mathematicaloptimization）而言，动态规划通常是指通过将决策分解为一系列子决策步骤来简化决策。
In terms of mathematical optimization, dynamic programming usually refers to simplifying a decision by breaking it down into a sequence of decision steps over time.在动态规划中，子问题解决方案被存储在一个表中，以便这些不必重新计算。
In dynamic programming, computed solutions to subproblems are stored in a table so that these don't have to be recomputed.这项策略包括所谓的"五个P"：多角度；适当定位；动态规划；可行的做法；以及策略意识；.
This strategy includes the so-called" five Ps": multiple perspectives;proper positioning; dynamic planning; practices that work; and awareness of ploys;通过采用自适应动态规划（ADP）技术，获得最优控制器而不依赖于系统动力学的知识。
By employing adaptive dynamic programming(ADP) technique, optimal controllers are obtained without relying on the knowledge of system dynamics.报告从增强学习，动态规划与现代非线性控制等技术出发，设计基于数据学习的自适应最优控制器。
Techniques from reinforcement learning, dynamic programming and modern nonlinear control are used to obtain a new class of learning-based adaptive optimal controllers.由于这种限制，它可以使用动态规划（4.7节），有效地找出最有可能的标记序列。
Given that restriction, it is possible to use dynamic programming(4.7) to efficiently find the most likely tag sequence.指数时间E2O(n)1.1n,10n使用动态规划解决旅行推销员问题.
Exponential time E 2O(n) 1.1n,10n Solving the traveling salesman problem using dynamic programming.在部分架构中，下以程式码也可由动态规划进行最佳化。
On some architectures,the code below can also be optimized by dynamic programming.接下来的三章描述了解决有限马尔可夫决策问题的三种基本方法：动态规划，蒙特卡洛方法和时间差异学习。
Chapter 3~5 describe three fundamental classes of methods forsolving finite Markov decision problems: dynamic programming, Monte Carlo methods, and temporal-difference learning.比如，我实现了一个物理引擎，但从未解决过动态规划的问题。
For example, I implemented a physics engine but never solved a dynamic programming problem.指数时间E2O(n)1.1n,10n使用动态规划解决旅行推销员问题.
(with linear exponent) E 2O(n) 1.1n,10n Solving the traveling salesman problem using dynamic programming.对于较小的n值，最优的r也可以通过动态规划方法得到。
For small values of n,the optimal r can also be obtained by standard dynamic programming methods.这是与动态规划的主要区别，动态规划是详尽的，并且保证能够找到解决方案。
This is the main difference from dynamic programming, which is exhaustive and is guaranteed to find the solution.用动态规划求解最优化问题的第一步就是刻画一个最优解的结构特征。
The first step in solving an optimization problem by dynamic programming is to characterize the structure of an optimal solution.加强了国家和国家以下各级分析并将人口特征和动态规划纳入发展和服务的能力.
Capacities at the national and subnational levels strengthened in analysing andintegrating population characteristics and dynamics planning into development and service delivery.我们遇到的问题中，有很大一部分可以用动态规划(简称DP)来解。
An important part 我们遇到的问题中，有很大一部分可以用动态规划(简称DP)来解。
An essential part 思路：这是很经典的一个问题，用动态规划解决。
This is a typical problem which can be solved by dynamic programming.在人工智能中环境通常被设为马尔可夫决策过程，所以许多强化学习算法在这种情况下使用动态规划技巧。
In machine learning, the environment is formulated as a Markov decision process(MDP),as many reinforcement learning algorithms for this context utilize dynamic programming techniques.在机器学习问题中，环境通常被规范为马可夫决策过程（MDP），所以许多强化学习算法在这种情况下使用动态规划技巧。
In machine learning, the environment is formulated as a Markov decision process(MDP),as many reinforcement learning algorithms for this context utilize dynamic programming techniques.人们通过命名贝尔曼方程（BellmanEquation）的方式来纪念Bellman的贡献，贝尔曼方程是动态规划的核心算法，它以递归形式重新描述了优化问题。
Bellman's contribution is remembered in the name of the Bellman equation,a central result of dynamic programming which restates an optimization problem in recursive form.
