### 机器学习代写|强化学习project代写reinforence learning代考|Intrinsic Motivation

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|Intrinsic Motivation

As stated before, another large branch of methods tackling the curse of sparse rewards is based on the idea of intrinsic motivation. These methods, similar to shaping, originate in behavioral science. Harlow [6] observed that even in the absence of

extrinsic rewards, monkeys have intrinsic drives, such as curiosity to solve complex puzzles. And these intrinsic drives can even be on par in strength with extrinsic incentives, such as food.

Singh et al. [12] transferred the notion of intrinsic motivation to reinforcement learning, illuminating it from an evolutionary perspective. Instead of postulating and hard-coding innate reward signals, an evolutionary-like process was run to optimize the reward function. The resulting reward functions turned out to incentivize exploration in addition to providing task-related guidance to the agent.

It is interesting to compare the computational discovery of Singh et al. [12] that the evolutionary optimal reward consists of two parts-one part responsible for providing motivation for solving a given task and the other part incentivizing exploration-with the way the reward signal is broken up in psychology into a primary reinforcer (basic needs) and a secondary reinforcer (abstract desires correlated with later satisfaction of basic needs). The primary reinforcer corresponds to the immediate physical reward defined by the environment the agent finds itself in. The secondary reinforcer corresponds to the evolutionary beneficial signal, which can be described as curiosity or desire for novelty/surprise, that helps the agent quickly adapt to variations in the environment.

Taking advantage of this two-part reward signal structure – task reward plus exploration bonus-Schmidhuber [11] proposed to design the exploration bonus directly, instead of performing costly evolutionary reward optimization. A variety of exploration bonuses have been described since then. Among the first ones were prediction error and improvement in the prediction error [11]. Recently, a large-scale study of curiosity-driven learning has been carried out [3], which showed that many problems, including Atari games and Mario, can be solved even without explicit task-specific rewards, by agents driven by pure curiosity.

However, curiosity is only one example of an intrinsic motivation signal. There is vast literature on intrinsic motivation, studying signals such as information gain, diversity, empowerment, and many more. We direct the interested reader to a comprehensive recent survey on intrinsic motivation in reinforcement learning [1] for further information.

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

Recent deep RL algorithms achieved impressive results, such as learning to play Atari games from pixels [27], how to walk [49] or reaching superhuman performance at chess, go and shogi [51]. However, a highly informative reward signal is typically necessary, and without it RL performs poorly, as shown in domains such as Montezuma’s Revenge [5].

The quality of the reward signal depends on multiple factors. First, the frequency at which rewards are emitted is crucial. Frequently emitted rewards are called “dense”, in contrast to infrequent emissions which are called “sparse”. Since improving the policy relies on getting feedback via rewards, the policy cannot be improved until a reward is obtained. In situations where this occurs very rarely, the agent can barely improve. Furthermore, even if the agent manages to obtain a reward, the feedback provided by it might still be less informative than the one of dense signals. In the case of infrequent rewards, in fact, it may be necessary to perform several action to achieve a reward. Hence, assigning credit to specific actions from a long sequence of actions is harder, since there are more actions to reason about.

One of the benchmarks for sparse rewards is the Arcade Learning Environment [5], which features several games with sparse rewards, such as Montezuma’s Revenge and Pitfall. The performance of most of RL algorithms in these games is poor, and

## 机器学习代写|强化学习project代写reinforence learning代考|Exploration Methods

Exploration methods aim to increase the agents knowledge about the environment. Since the agent starts off in an unknown environment, it is necessary to explore and gain knowledge about its dynamics and reward function. At any point the agent can exploit the current knowledge to gain the highest possible (to its current knowledge) cumulative reward. However, these two behaviours are conflicting ways of acting. Exploration is a long term endeavour where the agent tries to maximize the possibility of high rewards in the future, while exploitation is making use of the current knowledge and maximizing the expected rewards in the short term. The agent needs to strike a balance between these two contrasting behaviours, often referred to as “exploration-exploitation dilemma”.

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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。