### 机器学习代写|强化学习project代写reinforence learning代考| Instrumental Conditioning

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|Operant Conditioning

In instrumental conditioning, animals learn to modify their behavior in order to enforce a reward or to repress a punishment. The difference to classical conditioning is therefore that the animal does not receive the reward if he does not a perform desired action. As mentioned above, Thorndike already provided early evidence for this behavior in his law of effect. In some of the experiments, cats were put in puzzle boxes and they had to escape in order to receive a reward (like food). He noted that the cats initially tried actions that appeared random but gradually started to stamp out behavior which was not successful and stamp in rewarding behavior. As one could imagine, the cat became faster after a while. This showed that the cats were learning by trial and error and Thorndike called this the “law of effect”. The idea of the law of effect corresponds to learning algorithms that select among different alternatives and that actions on specific states are associated with a reward or even a right step to the expected future reward. Influenced by Thorndike’s research, Hull and Skinner argued that behavior is selected on the basis of the consequences they produce and coined the term operant conditioning. For his experiments, Skinner invented what is now called Skinner’s box in which he put pigeons that can press a lever in order to get a reward. Skinner further popularized what he called the process of shaping. Shaping occurs when the trainer rewards the agent with any taken action that has a slight resemblance to the desired behavior and this process converged to the correct result when applied to pigeons [21]. This process can be directly mapped to reward shaping in reinforcement learning.

## 机器学习代写|强化学习project代写reinforence learning代考|Neural View

Neuroscience is the field that is concerned with studying the structure and function of the central nervous system including the brain. Neurons are the basic building blocks of brains and, unlike other cells, are densely interconnected. On average each neuron has 7000 synaptic connections and the cerebral cortex alone (the folded outer layer of the brain) is estimated to have $1.5 \times 10^{14}$ synapses [5]. Synaptic connections can be of a chemical or an electrical nature. We concentrate on the former because they are a basis for synaptic plasticity which is correlated with learning [7]. According to the Hebbian theory, repeated stimulation of the postsynaptic neurons increases or decreases the synaptic efficacy. Chemical communication occurs through the synapses by secreting neurotransmitters from the presynaptic cell to receptors on the postsynaptic cell through the synaptic cleft. Fig. 2 shows an illustration of such a chemical synapse. The effect of these neurotransmitters on the postsynaptic neurons can be of an excitatory or an inhibitory nature. Dopamine is perhaps the most famous neurotransmitter. Dopamine plays a role in multiple brain areas and is correlated with different brain functions including learning and will be discussed further in the subsections below. A key feature that makes dopamine a promising candidate to be involved with learning is that the dopamine system is a neuromodulator. Neuromodulators are not as restricted as excitatory or inhibitory neurotransmitters and can reach distant regions in the CNS and affect large numbers of neurons simultaneously.

## 机器学习代写|强化学习project代写reinforence learning代考|Reward Prediction Error Hypothesis

Work by Schultz et al. and others have shown that there is a strong similarity between the phasic activation of midbrain dopamine neurons and the prediction error $\delta[20]$. They showed that when an animal receives an unpredicted reward, dopamine neuron activity increases substantially. After the conditioning phase, the neuronal activity relocates to the moment when the $\mathrm{CS}$ is presented and not of the reward itself. If the $\mathrm{CS}$ is presented but with omitting the reward afterwards, a decrease of the activity below the baseline is observed approximately at the moment when the reward was presented during conditioning. These observations are consistent with the concept of prediction error. Findings from functional Magnetic Resonance Imaging (fMRI) have shown activation correlated with prediction errors in the striatum and the orbitofrontal cortex [2]. The presence or absence of activity related to prediction errors in the striatum distinguishes participants who learn to perform optimally from those who do not [18].

## 有限元方法代写

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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。