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第 4 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8 小时

建议:4-6 hours/week...

英语(English)

字幕:英语(English)

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 4 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8 小时

建议:4-6 hours/week...

英语(English)

字幕:英语(English)

教学大纲 - 您将从这门课程中学到什么

1

1

完成时间为 1 小时

Welcome to the Final Capstone Course!

完成时间为 1 小时
2 个视频 (总计 10 分钟), 2 个阅读材料
2 个视频
Meet your instructors!8分钟
2 个阅读材料
Reinforcement Learning Textbook10分钟
Pre-requisites and Learning Objectives10分钟
2

2

完成时间为 1 小时

Milestone 1: Formalize Word Problem as MDP

完成时间为 1 小时
4 个视频 (总计 23 分钟)
4 个视频
Andy Barto on What are Eligibility Traces and Why are they so named?9分钟
Let's Review: Markov Decision Processes6分钟
Let's Review: Examples of Episodic and Continuing Tasks3分钟
3

3

完成时间为 1 小时

Milestone 2: Choosing The Right Algorithm

完成时间为 1 小时
7 个视频 (总计 40 分钟)
7 个视频
Let's Review: Expected Sarsa3分钟
Let's Review: What is Q-learning?3分钟
Let's Review: Average Reward- A New Way of Formulating Control Problems10分钟
Let's Review: Actor-Critic Algorithm5分钟
Csaba Szepesvari on Problem Landscape8分钟
Andy and Rich: Advice for Students5分钟
1 个练习
Choosing the Right Algorithm
4

4

完成时间为 1 小时

Milestone 3: Identify Key Performance Parameters

完成时间为 1 小时
4 个视频 (总计 25 分钟)
4 个视频
Let's Review: Non-linear Approximation with Neural Networks4分钟
Drew Bagnell on System ID + Optimal Control6分钟
Susan Murphy on RL in Mobile Health7分钟
1 个练习
Impact of Parameter Choices in RL40分钟

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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

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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