课程信息

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第 1 门课程(共 4 门)
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中级

Probabilities & Expectations, basic linear algebra, basic calculus, Python 3.0 (at least 1 year), implementing algorithms from pseudocode.

完成时间大约为15 小时
英语(English)
字幕:英语(English)

您将学到的内容有

  • Formalize problems as Markov Decision Processes

  • Understand basic exploration methods and the exploration / exploitation tradeoff

  • Understand value functions, as a general-purpose tool for optimal decision-making

  • Know how to implement dynamic programming as an efficient solution approach to an industrial control problem

您将获得的技能

Artificial Intelligence (AI)Machine LearningReinforcement LearningFunction ApproximationIntelligent Systems
可分享的证书
完成后获得证书
100% 在线
立即开始,按照自己的计划学习。
第 1 门课程(共 4 门)
可灵活调整截止日期
根据您的日程表重置截止日期。
中级

Probabilities & Expectations, basic linear algebra, basic calculus, Python 3.0 (at least 1 year), implementing algorithms from pseudocode.

完成时间大约为15 小时
英语(English)
字幕:英语(English)

提供方

阿尔伯塔大学 徽标

阿尔伯塔大学

Alberta Machine Intelligence Institute 徽标

Alberta Machine Intelligence Institute

教学大纲 - 您将从这门课程中学到什么

内容评分Thumbs Up93%(7,084 个评分)Info
1

1

完成时间为 1 小时

Welcome to the Course!

完成时间为 1 小时
4 个视频 (总计 20 分钟), 2 个阅读材料
4 个视频
Course Introduction5分钟
Meet your instructors!8分钟
Your Specialization Roadmap3分钟
2 个阅读材料
Reinforcement Learning Textbook10分钟
Read Me: Pre-requisites and Learning Objectives10分钟
完成时间为 4 小时

The K-Armed Bandit Problem

完成时间为 4 小时
8 个视频 (总计 46 分钟), 3 个阅读材料, 2 个测验
8 个视频
Learning Action Values4分钟
Estimating Action Values Incrementally5分钟
What is the trade-off?7分钟
Optimistic Initial Values6分钟
Upper-Confidence Bound (UCB) Action Selection5分钟
Jonathan Langford: Contextual Bandits for Real World Reinforcement Learning8分钟
Week 1 Summary3分钟
3 个阅读材料
Module 2 Learning Objectives10分钟
Weekly Reading30分钟
Chapter Summary30分钟
1 个练习
Exploration/Exploitation45分钟
2

2

完成时间为 3 小时

Markov Decision Processes

完成时间为 3 小时
7 个视频 (总计 36 分钟), 2 个阅读材料, 2 个测验
7 个视频
Examples of MDPs4分钟
The Goal of Reinforcement Learning3分钟
Michael Littman: The Reward Hypothesis12分钟
Continuing Tasks5分钟
Examples of Episodic and Continuing Tasks3分钟
Week 2 Summary1分钟
2 个阅读材料
Module 3 Learning Objectives10分钟
Weekly Reading30分钟
1 个练习
MDPs45分钟
3

3

完成时间为 3 小时

Value Functions & Bellman Equations

完成时间为 3 小时
9 个视频 (总计 56 分钟), 3 个阅读材料, 2 个测验
9 个视频
Value Functions6分钟
Rich Sutton and Andy Barto: A brief History of RL7分钟
Bellman Equation Derivation6分钟
Why Bellman Equations?5分钟
Optimal Policies7分钟
Optimal Value Functions5分钟
Using Optimal Value Functions to Get Optimal Policies8分钟
Week 3 Summary4分钟
3 个阅读材料
Module 4 Learning Objectives10分钟
Weekly Reading30分钟
Chapter Summary13分钟
2 个练习
Value Functions and Bellman Equations45分钟
Value Functions and Bellman Equations45分钟
4

4

完成时间为 4 小时

Dynamic Programming

完成时间为 4 小时
10 个视频 (总计 72 分钟), 3 个阅读材料, 2 个测验
10 个视频
Iterative Policy Evaluation8分钟
Policy Improvement4分钟
Policy Iteration8分钟
Flexibility of the Policy Iteration Framework4分钟
Efficiency of Dynamic Programming5分钟
Warren Powell: Approximate Dynamic Programming for Fleet Management (Short)7分钟
Warren Powell: Approximate Dynamic Programming for Fleet Management (Long)21分钟
Week 4 Summary2分钟
Congratulations!3分钟
3 个阅读材料
Module 5 Learning Objectives10分钟
Weekly Reading30分钟
Chapter Summary30分钟
1 个练习
Dynamic Programming45分钟

审阅

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关于 强化学习 专项课程

The Reinforcement Learning Specialization consists of 4 courses exploring the power of adaptive learning systems and artificial intelligence (AI). Harnessing the full potential of artificial intelligence requires adaptive learning systems. Learn how Reinforcement Learning (RL) solutions help solve real-world problems through trial-and-error interaction by implementing a complete RL solution from beginning to end. By the end of this Specialization, learners will understand the foundations of much of modern probabilistic artificial intelligence (AI) and be prepared to take more advanced courses or to apply AI tools and ideas to real-world problems. This content will focus on “small-scale” problems in order to understand the foundations of Reinforcement Learning, as taught by world-renowned experts at the University of Alberta, Faculty of Science. The tools learned in this Specialization can be applied to game development (AI), customer interaction (how a website interacts with customers), smart assistants, recommender systems, supply chain, industrial control, finance, oil & gas pipelines, industrial control systems, and more....
强化学习

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