statistics-lab™ 为您的留学生涯保驾护航 在代写强化学习reinforence learning方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写强化学习reinforence learning代写方面经验极为丰富，各种代写强化学习reinforence learning相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (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.

## 机器学习代写|强化学习project代写reinforence learning代考|Evolutionary Reward Signals: Survival and Fitness

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。

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## 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。