### 机器学习代写|强化学习project代写reinforence learning代考|Prediction Error and Actor-Critic

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• 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|Hypotheses in the Brain

Abstract Humans, as well as other life forms, can be seen as agents in nature who interact with their environment to gain rewards like pleasure and nutrition. This view has parallels with reinforcement learning from computer science and engineering. Early developments in reinforcement learning were inspired by intuitions from animal learning theories. More recent research in computational neuroscience has borrowed ideas that come from reinforcement learning to better understand the function of the mammalian brain during learning. In this report, we will compare computational, behavioral, and neural views of reinforcement learning. For each view we start by introducing the field and discuss the problems of prediction and control while focusing on the temporal difference learning method and the actor-critic paradigm. Based on the literature survey, we then propose a hypothesis for learning in the brain using multiple critics.

While science is the systematic study of natural phenomena, technology is often inspired by our observations of them. Computer scientists for example have developed algorithms based on behavior of animals and insects. On the other hand, sometimes developments from mathematics and pure reasoning find connections in nature afterwards. The actor-critic hypothesis of learning in the brain is an example of the latter case.

This report is composed of the three views of behaviorism from psychology, (computational) neuroscience from biology, and reinforcement learning from computer science and engineering. Each view is divided into the problems of prediction and control. The goal of prediction is to measure an expected value like a reward. The goal of control is to find an optimal strategy that maximizes the expected reward. We begin the discussion with the computational view in Sect. 2 by specifying the underlying framework and introducing Temporal Difference learning for prediction and the actor-critic method for control. Next we discuss the behavioral view in Sect. $3 .$ There we will highlight historical developments of two conditioning (i.e learning) theories in animals. These two theories, called classical conditioning and instrumental conditioning, can be directly mapped to prediction and control. Furthermore, we discuss the neuroscientific view in Sect. 4. In this section, we discuss the prediction error and actor-critic hypotheses in the brain. Finally, we propose further research into the interaction between different regions associated with the critic in the brain. Before we conclude, we will highlight some limitations within the neuroscientific view.

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

Reinforcement learning (RL) in computer science and engineering is the branch of machine learning that deals with decision making. For this view we use the Markov decision process (MDP) as the underlying framework. MDP is defined mathematically as the tuple $(S, A, P, R)$. An agent that observes a state $s_{t} \in S$ of the environment at time $t$. The agent can then interact with the environment by taking action $a \in A$. The results of this interaction yields a reward $r(s, a) \in R$ which depends on the current state $s$ produced by taking the action $a$. At the same time the action can cause a state transition. In this case the resulting state $s_{t+1}$ is produced according to state transition model $P$, which defines the probability of reaching state $s_{t+1}$ when taking action $a$ on state $s$. The goal of the agent is then to learn a policy $\pi$ that maximizes the cumulative reward. A key difference to supervised learning is that RL deals with data that is dynamically generated by the agent as opposed to having a fixed set already available beforehand.

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

Behaviorism is a branch of psychology that focuses on reproducible behavior in animals. Thorndike wrote in 1898 about animal intelligence based on his experiments that were used to study associative behaviour in animals [26]. He formulated the law of effect which states that responses that produce rewards tend to occur more likely given a similar situation and responses that produce punishments tend to be avoided in the future when given a similar situation. In behavioral psychology, there are two different concepts of conditioning (i.e. learning) called classical and operant conditioning. These two concepts can be mapped to prediction and control in reinforcement learning and will be discussed in the subsections below.

Animal behavior, as well as their underlying neural substrates, consists of complicated and not fully understood mechanisms. There are many, possibly antagonist processes in biology happening simultaneously as opposed to artificial agents that implement idealized computational algorithms. This shows that the difference between the function of artificial and biological agents should not be taken for granted. Furthermore, there is an unresolved gap in the relationship between subjective experience of (biological) agents and measurable neural activity [4].

Classical conditioning, sometimes referred to as Pavlovian conditioning, is a type of learning documented by Ivan Pavlov in the mid-20th century during his experiments with dogs [15]. In classical conditioning, animals learn by associating stimuli with rewards. In order to understand how animals can learn to predict rewards, we invoke terminology from Pavlov’s experiments:

• Unconditioned Stimulus (US): A dog is presented with a reward, for example a piece of meat.
• Unconditioned Response $(U R)$ : Shortly after noticing the meat, the dog starts to salivate.
• Neutral Stimulus (NS): The dog hears a unique sound. We will assume its the sound of a bell. Neutral here means that it does not initially produce a specific response relevant for the experiment.
• Conditioning: The dog is repeatedly presented with meat and the bell sound simultaneously.
• Conditioned Stimulus (CS): Now the bell has been paired with the expectation of getting the reward.
• Conditioned Response (SR): Subsequently, when the dog hears the sound of the bell, he starts to salivate. Here we can assume that the dog has learned to predict the reward.

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

• 无条件刺激（美国）：向狗提供奖励，例如一块肉。
• 无条件反应(在R): 注意到肉后不久，狗开始​​流口水。
• 中性刺激（NS）：狗听到独特的声音。我们假设它是铃声。这里的中性意味着它最初不会产生与实验相关的特定响应。
• 调理：狗被反复呈现肉和铃声同时响起。
• 条件刺激（CS）：现在已经与获得奖励的期望配对。
• 条件反应（SR）：随后，当狗听到铃声时，他开始流口水。在这里，我们可以假设狗已经学会了预测奖励。

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## MATLAB代写

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