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强化学习是一种基于奖励期望行为和/或惩罚不期望行为的机器学习训练方法。一般来说,强化学习代理能够感知和解释其环境,采取行动并通过试验和错误学习。
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我们提供的强化学习reinforence learning及其相关学科的代写,服务范围广, 其中包括但不限于:
- Statistical Inference 统计推断
- Statistical Computing 统计计算
- Advanced Probability Theory 高等概率论
- Advanced Mathematical Statistics 高等数理统计学
- (Generalized) Linear Models 广义线性模型
- Statistical Machine Learning 统计机器学习
- Longitudinal Data Analysis 纵向数据分析
- Foundations of Data Science 数据科学基础
机器学习代写|强化学习project代写reinforence learning代考|Reinforcement Learning
Abstract The reward signal is responsible for determining the agent’s behavior, and therefore is a crucial element within the reinforcement learning paradigm. Nevertheless, the mainstream of RL research in recent years has been preoccupied with the development and analysis of learning algorithms, treating the reward signal as given and not subject to change. As the learning algorithms have matured, it is now time to revisit the questions of reward function design. Therefore, this chapter reviews the history of reward function design, highlighting the links to behavioral sciences and evolution, and surveys the most recent developments in RL. Reward shaping, sparse and dense rewards, intrinsic motivation, curiosity, and a number of other approaches are analyzed and compared in this chapter.
With the sharp increase of interest in machine learning in recent years, the field of reinforcement learning (RL) has also gained a lot of traction. Reinforcement learning is generally thought to be particularly promising, because it provides a constructive, optimization-based formalization of the behavior learning problem that is applicable to a large class of systems. Mathematically, the RL problem is represented by a Markov decision process (MDP) whose transition dynamics and/or the reward function are unknown to the agent.
The reward function, being an essential part of the MDP definition, can be thought of as ranking various proposal behaviors. The goal of a learning agent is then to find the behavior with the highest rank. However, there is often a discrepancy between a task and a reward function. For example, a task for a robot may be to open a door; the success in such a task can be evaluated by a binary function that returns one if the door is eventually open and zero otherwise. In practice, though, the reward function
can be made more informative, including such terms as the proximity to the door handle and the force applied to the door to open it. In the former case, we are dealing with a sparse reward scenario, and in the latter case, we have a dense reward scenario. Is the dense reward better for learning? If yes, how to design a dense reward with desired properties? Are there any requirements that the dense reward has to satisfy if what one really cares about is the sparse reward formulation? Such and related questions constitute the focus of this chapter.
At the end of the day, it is the engineer who has to decide on the reward function. Figure 1 shows a typical RL project structure, highlighting the key interactions between its parts. A feedback loop passing through the engineer is especially emphasized, showing that the reward function and the learning algorithm are typically adjusted by the engineer in an iterative fashion based on the given task. The environment, on the other hand, which is identified with the system dynamics in this chapter, is depicted as being outside of engineer’s control, reflecting the situation in real-world applications of reinforcement learning. This chapter reviews and systematizes techniques of reward function design to provide practical guidance to the engineer.
机器学习代写|强化学习project代写reinforence learning代考|Evolutionary Reward Signals: Survival and Fitness
Biological evolution is an example of a process where the reward signal is hard to quantify. At the same time, it is perhaps the oldest learning algorithm and therefore has been studied very thoroughly. As one of the first computational modeling approaches, Smith [14] builds a connection between mathematical optimization and biological evolution. He mainly tries to explain the outcome of evolution by identifying the main characteristics of an optimization problem: a set of constraints, an optimization criterion, and heredity. He focuses very much on the individual and identifies the reproduction rate, gait(s), and the foraging strategy as major constraints. These constraints are supposed to cover the control distribution and what would be the dynamics equations in classical control. For the optimization criterion, he chooses the inclusive fitness, which again is a measure of reproduction capabilities. Thus, he takes a very fine-grained view that does not account for long-term behavior but rather falls back to a “greedy” description of the individual.
Reiss [10] criticizes this very simplistic understanding of fitness and acknowledges that the measurement of fitness is virtually impossible in reality. More recently, Grafen [5] attempts to formalize the inclusive notion of the fitness definition. He states that inclusive fitness is only understood in a narrow set of simple situations and even questions whether it is maximized by natural selection at all. To circumvent the direct specification of fitness, another, more abstract, view can be taken. Here, the process is treated as not being fully observable. It is sound to assume that just the rules of physics – which induce, among other things, the concept of survival-form a strict framework, where the survival of an individual is extremely noisy but its fitness is a consistent (probabilistic) latent variable.
From this perspective, survival can be seen as an extremely sparse reward signal. When viewing a human population as an agent, it becomes apparent that the agent not only learned to model its environment (e.g., using science) and to improve itself (e.g., via sexual selection), but also to invent and inherit cultural traditions (e.g., via intergenerational knowledge transfer). In reinforcement learning terms, it is hard to determine the horizon/discounting rate on the population and even on the individual scale. Even considering only a small set of particular choices of an individuum, different studies come to extremely different results, as shown in [4].
So there is no definitive answer on how to specify the reward function and discounting scheme of the natural evolution in terms of a (multi-agent) reinforcement learning setup.
机器学习代写|强化学习project代写reinforence learning代考|Monetary Reward in Economics
In contrast to the biological evolution discussed in Sect. 2.1, the reward function arises quite naturally in economics. Simply put, the reward can be identified with the amount of money. As stated by Hughes [7], the learning aspect is really important a
in the economic setup, because albeit many different models exist for financial markets, these are in most cases based on coarse-grained macroeconomic or technical indicators [2]. Since only an extremely small fraction of a market can be captured by direct observation, the agent should learn the mechanics of a particular environment implicitly by taking actions and receiving the resulting reward.
An agent trading in a market and receiving the increase/decrease in value of its assets as the reward at each time-step is also an example for a setup with a dense (as opposed to sparse) reward signal. At every time-step, there is some (arguably unbiased) signal of its performance. In this case, the density of the reward signal increases with the liquidity of the particular market. This example still leaves the question of discounting open. But in economic problems, the discounting rate has the interpretation of an interest-/inflation-rate and should be viewed as dictated by the environment rather than chosen as a learning parameter in most cases. This is also implied by the usage of the term ‘discounting’ in economics where, e.g., the discounted cash flow analysis is based on essentially the same interpretation.
强化学习代写
机器学习代写|强化学习project代写reinforence learning代考|Reinforcement Learning
摘要奖励信号负责确定代理的行为,因此是强化学习范式中的关键元素。尽管如此,近年来 RL 研究的主流一直专注于学习算法的开发和分析,将奖励信号视为给定的,不会发生变化。随着学习算法的成熟,现在是重新审视奖励函数设计问题的时候了。因此,本章回顾了奖励函数设计的历史,突出了与行为科学和进化的联系,并调查了 RL 的最新发展。本章分析和比较了奖励塑造、稀疏和密集奖励、内在动机、好奇心和许多其他方法。
随着近年来对机器学习的兴趣急剧增加,强化学习(RL)领域也获得了很大的关注。强化学习通常被认为是特别有前途的,因为它提供了适用于一大类系统的行为学习问题的建设性、基于优化的形式化。在数学上,RL 问题由马尔可夫决策过程 (MDP) 表示,其转换动态和/或奖励函数对于代理来说是未知的。
奖励函数是 MDP 定义的重要组成部分,可以认为是对各种提案行为进行排序。学习代理的目标是找到排名最高的行为。然而,任务和奖励函数之间经常存在差异。例如,机器人的任务可能是开门;这种任务的成功可以通过一个二进制函数来评估,如果门最终打开,则返回 1,否则返回 0。但在实践中,奖励函数
可以提供更多信息,包括与门把手的接近程度以及施加在门上以打开门的力等术语。在前一种情况下,我们处理的是稀疏奖励场景,而在后一种情况下,我们处理的是密集奖励场景。密集奖励更适合学习吗?如果是,如何设计具有所需属性的密集奖励?如果人们真正关心的是稀疏奖励公式,那么密集奖励是否必须满足任何要求?这些和相关的问题构成了本章的重点。
归根结底,必须由工程师来决定奖励功能。图 1 显示了一个典型的 RL 项目结构,突出了其部分之间的关键交互。特别强调了通过工程师的反馈循环,表明奖励函数和学习算法通常由工程师根据给定任务以迭代方式调整。另一方面,本章中与系统动力学相关的环境被描述为工程师无法控制的,反映了强化学习在现实世界应用中的情况。本章对奖励函数设计技术进行回顾和系统梳理,为工程师提供实践指导。
机器学习代写|强化学习project代写reinforence learning代考|Evolutionary Reward Signals: Survival and Fitness
生物进化是奖励信号难以量化的过程的一个例子。同时,它可能是最古老的学习算法,因此已经被研究得非常透彻。作为最早的计算建模方法之一,Smith [14] 在数学优化和生物进化之间建立了联系。他主要试图通过识别优化问题的主要特征来解释进化的结果:一组约束、一个优化标准和遗传。他非常关注个体,并将繁殖率、步态和觅食策略确定为主要限制因素。这些约束应该涵盖控制分布以及经典控制中的动力学方程。对于优化标准,他选择了包容性适应度,这又是衡量繁殖能力的指标。因此,他采取了一种非常细粒度的观点,不考虑长期行为,而是回归到对个人的“贪婪”描述。
Reiss [10] 批评了这种对适应度非常简单的理解,并承认在现实中测量适应度几乎是不可能的。最近,Grafen [5] 试图将适应度定义的包容性概念正式化。他指出,仅在一组狭窄的简单情况下才能理解包容性适应度,甚至质疑它是否完全通过自然选择而最大化。为了规避适应度的直接说明,可以采用另一种更抽象的观点。在这里,该过程被视为不可完全观察。假设只有物理学规则——其中包括生存的概念——形成一个严格的框架是合理的,其中个体的生存是非常嘈杂的,但它的适应度是一个一致的(概率)潜在变量。
从这个角度来看,生存可以看作是一个极其稀疏的奖励信号。当将人类群体视为代理人时,很明显代理人不仅学会了对其环境进行建模(例如,使用科学)和改进自己(例如,通过性选择),而且还发明和继承了文化传统(例如,通过代际知识转移)。在强化学习方面,很难确定总体甚至个人规模的水平/贴现率。即使只考虑一个个体的一小部分特定选择,不同的研究也会得出截然不同的结果,如 [4] 所示。
因此,对于如何根据(多智能体)强化学习设置来指定自然进化的奖励函数和折扣方案,没有明确的答案。
机器学习代写|强化学习project代写reinforence learning代考|Monetary Reward in Economics
与 Sect 中讨论的生物进化相反。2.1,奖励函数在经济学中很自然地出现。简单地说,奖励可以用金额来确定。正如 Hughes [7] 所说,学习方面非常重要。
在经济设置中,尽管金融市场存在许多不同的模型,但在大多数情况下,这些模型都是基于粗粒度的宏观经济或技术指标 [2]。由于直接观察只能捕捉到市场的极小部分,因此代理应该通过采取行动并接收由此产生的奖励来隐式地学习特定环境的机制。
代理在市场上交易并在每个时间步接收其资产价值的增加/减少作为奖励,这也是具有密集(而不是稀疏)奖励信号的设置的示例。在每个时间步,都有一些(可以说是无偏见的)其性能信号。在这种情况下,奖励信号的密度随着特定市场的流动性而增加。这个例子仍然没有解决折扣问题。但在经济问题中,贴现率具有利率/通货膨胀率的解释,应被视为由环境决定,而不是在大多数情况下被选为学习参数。经济学中“贴现”一词的使用也暗示了这一点,例如,贴现现金流分析基于基本相同的解释。
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金融工程代写
金融工程是使用数学技术来解决金融问题。金融工程使用计算机科学、统计学、经济学和应用数学领域的工具和知识来解决当前的金融问题,以及设计新的和创新的金融产品。
非参数统计代写
非参数统计指的是一种统计方法,其中不假设数据来自于由少数参数决定的规定模型;这种模型的例子包括正态分布模型和线性回归模型。
广义线性模型代考
广义线性模型(GLM)归属统计学领域,是一种应用灵活的线性回归模型。该模型允许因变量的偏差分布有除了正态分布之外的其它分布。
术语 广义线性模型(GLM)通常是指给定连续和/或分类预测因素的连续响应变量的常规线性回归模型。它包括多元线性回归,以及方差分析和方差分析(仅含固定效应)。
有限元方法代写
有限元方法(FEM)是一种流行的方法,用于数值解决工程和数学建模中出现的微分方程。典型的问题领域包括结构分析、传热、流体流动、质量运输和电磁势等传统领域。
有限元是一种通用的数值方法,用于解决两个或三个空间变量的偏微分方程(即一些边界值问题)。为了解决一个问题,有限元将一个大系统细分为更小、更简单的部分,称为有限元。这是通过在空间维度上的特定空间离散化来实现的,它是通过构建对象的网格来实现的:用于求解的数值域,它有有限数量的点。边界值问题的有限元方法表述最终导致一个代数方程组。该方法在域上对未知函数进行逼近。[1] 然后将模拟这些有限元的简单方程组合成一个更大的方程系统,以模拟整个问题。然后,有限元通过变化微积分使相关的误差函数最小化来逼近一个解决方案。
tatistics-lab作为专业的留学生服务机构,多年来已为美国、英国、加拿大、澳洲等留学热门地的学生提供专业的学术服务,包括但不限于Essay代写,Assignment代写,Dissertation代写,Report代写,小组作业代写,Proposal代写,Paper代写,Presentation代写,计算机作业代写,论文修改和润色,网课代做,exam代考等等。写作范围涵盖高中,本科,研究生等海外留学全阶段,辐射金融,经济学,会计学,审计学,管理学等全球99%专业科目。写作团队既有专业英语母语作者,也有海外名校硕博留学生,每位写作老师都拥有过硬的语言能力,专业的学科背景和学术写作经验。我们承诺100%原创,100%专业,100%准时,100%满意。
随机分析代写
随机微积分是数学的一个分支,对随机过程进行操作。它允许为随机过程的积分定义一个关于随机过程的一致的积分理论。这个领域是由日本数学家伊藤清在第二次世界大战期间创建并开始的。
时间序列分析代写
随机过程,是依赖于参数的一组随机变量的全体,参数通常是时间。 随机变量是随机现象的数量表现,其时间序列是一组按照时间发生先后顺序进行排列的数据点序列。通常一组时间序列的时间间隔为一恒定值(如1秒,5分钟,12小时,7天,1年),因此时间序列可以作为离散时间数据进行分析处理。研究时间序列数据的意义在于现实中,往往需要研究某个事物其随时间发展变化的规律。这就需要通过研究该事物过去发展的历史记录,以得到其自身发展的规律。
回归分析代写
多元回归分析渐进(Multiple Regression Analysis Asymptotics)属于计量经济学领域,主要是一种数学上的统计分析方法,可以分析复杂情况下各影响因素的数学关系,在自然科学、社会和经济学等多个领域内应用广泛。
MATLAB代写
MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中,其中问题和解决方案以熟悉的数学符号表示。典型用途包括:数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发,包括图形用户界面构建MATLAB 是一个交互式系统,其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题,尤其是那些具有矩阵和向量公式的问题,而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问,这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展,得到了许多用户的投入。在大学环境中,它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域,MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要,工具箱允许您学习和应用专业技术。工具箱是 MATLAB 函数(M 文件)的综合集合,可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。