## 課程信息

109,024 次近期查看

100% 在線

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

## 您將獲得的技能

Artificial Intelligence (AI)Machine LearningReinforcement LearningFunction ApproximationIntelligent Systems

100% 在線

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

1

## Welcome to the Course!

2 個視頻 （總計 10 分鐘）, 2 個閱讀材料
2 個視頻
2 個閱讀材料
Reinforcement Learning Textbook10分鐘
Read Me: Pre-requisites and Learning Objectives10分鐘
2

## Monte Carlo Methods for Prediction & Control

11 個視頻 （總計 58 分鐘）, 2 個閱讀材料, 1 個測驗
11 個視頻
Using Monte Carlo for Prediction6分鐘
Using Monte Carlo for Action Values2分鐘
Using Monte Carlo methods for generalized policy iteration2分鐘
Solving the Blackjack Example3分鐘
Epsilon-soft policies5分鐘
Why does off-policy learning matter?4分鐘
Importance Sampling4分鐘
Off-Policy Monte Carlo Prediction5分鐘
Emma Brunskill: Batch Reinforcement Learning12分鐘
Week 1 Summary3分鐘
2 個閱讀材料
Chapter Summary40分鐘
1 個練習
3

## Temporal Difference Learning Methods for Prediction

6 個視頻 （總計 37 分鐘）, 1 個閱讀材料, 2 個測驗
6 個視頻
Rich Sutton: The Importance of TD Learning6分鐘
The advantages of temporal difference learning5分鐘
Comparing TD and Monte Carlo5分鐘
Andy Barto and Rich Sutton: More on the History of RL12分鐘
Week 2 Summary2分鐘
1 個閱讀材料
1 個練習
Practice Quiz30分鐘
4

## Temporal Difference Learning Methods for Control

9 個視頻 （總計 30 分鐘）, 2 個閱讀材料, 2 個測驗
9 個視頻
Sarsa in the Windy Grid World3分鐘
What is Q-learning?3分鐘
Q-learning in the Windy Grid World3分鐘
How is Q-learning off-policy?4分鐘
Expected Sarsa3分鐘
Expected Sarsa in the Cliff World3分鐘
Generality of Expected Sarsa1分鐘
Week 3 Summary2分鐘
2 個閱讀材料
Chapter summary40分鐘
1 個練習
Practice Quiz30分鐘

## 關於 强化学习 專項課程

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....

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