About this Course
최근 조회 64,189

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

다음 전문 분야의 4개 강좌 중 3번째 강좌:

유동적 마감일

일정에 따라 마감일을 재설정합니다.

중급 단계

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

완료하는 데 약 19시간 필요

권장: 4-6 hours/week...

영어

자막: 영어

귀하가 습득할 기술

Artificial Intelligence (AI)Machine LearningReinforcement LearningFunction ApproximationIntelligent Systems

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

다음 전문 분야의 4개 강좌 중 3번째 강좌:

유동적 마감일

일정에 따라 마감일을 재설정합니다.

중급 단계

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

완료하는 데 약 19시간 필요

권장: 4-6 hours/week...

영어

자막: 영어

강의 계획 - 이 강좌에서 배울 내용

1
완료하는 데 1시간 필요

Welcome to the Course!

2개 동영상 (총 12분), 2 readings
2개의 동영상
Meet your instructors!8m
2개의 읽기 자료
Read Me: Pre-requisites and Learning Objectives10m
Reinforcement Learning Textbook10m
완료하는 데 6시간 필요

On-policy Prediction with Approximation

13개 동영상 (총 69분), 1 reading, 2 quizzes
13개의 동영상
Generalization and Discrimination5m
Framing Value Estimation as Supervised Learning3m
The Value Error Objective4m
Introducing Gradient Descent7m
Gradient Monte for Policy Evaluation5m
State Aggregation with Monte Carlo7m
Semi-Gradient TD for Policy Evaluation3m
Comparing TD and Monte Carlo with State Aggregation4m
Doina Precup: Building Knowledge for AI Agents with Reinforcement Learning7m
The Linear TD Update3m
The True Objective for TD5m
Week 1 Summary4m
1개의 읽기 자료
Weekly Reading: On-policy Prediction with Approximation40m
1개 연습문제
On-policy Prediction with Approximation30m
2
완료하는 데 8시간 필요

Constructing Features for Prediction

11개 동영상 (총 52분), 1 reading, 2 quizzes
11개의 동영상
Generalization Properties of Coarse Coding5m
Tile Coding3m
Using Tile Coding in TD4m
What is a Neural Network?3m
Non-linear Approximation with Neural Networks4m
Deep Neural Networks3m
Gradient Descent for Training Neural Networks8m
Optimization Strategies for NNs4m
David Silver on Deep Learning + RL = AI?9m
Week 2 Review2m
1개의 읽기 자료
Weekly Reading: On-policy Prediction with Approximation II40m
1개 연습문제
Constructing Features for Prediction28m
3
완료하는 데 8시간 필요

Control with Approximation

7개 동영상 (총 41분), 1 reading, 2 quizzes
7개의 동영상
Episodic Sarsa in Mountain Car5m
Expected Sarsa with Function Approximation2m
Exploration under Function Approximation3m
Average Reward: A New Way of Formulating Control Problems10m
Satinder Singh on Intrinsic Rewards12m
Week 3 Review2m
1개의 읽기 자료
Weekly Reading: On-policy Control with Approximation40m
1개 연습문제
Control with Approximation40m
4
완료하는 데 6시간 필요

Policy Gradient

11개 동영상 (총 55분), 1 reading, 2 quizzes
11개의 동영상
Advantages of Policy Parameterization5m
The Objective for Learning Policies5m
The Policy Gradient Theorem5m
Estimating the Policy Gradient4m
Actor-Critic Algorithm5m
Actor-Critic with Softmax Policies3m
Demonstration with Actor-Critic6m
Gaussian Policies for Continuous Actions7m
Week 4 Summary3m
Congratulations! Course 4 Preview2m
1개의 읽기 자료
Weekly Reading: Policy Gradient Methods40m
1개 연습문제
Policy Gradient Methods45m
4.8
15개의 리뷰Chevron Right

Prediction and Control with Function Approximation의 최상위 리뷰

대학: ABNov 5th 2019

Great Learning, the best part was the Actor-Critic algorithm for a small pendulum swing task all from stratch using RLGLue library. Love to learn how experimentation in RL works.

대학: IFNov 10th 2019

Great course. Slightly more complex than courses 1 and 2, but a huge improvement in terms of applicability to real-world situations.

강사

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Martha White

Assistant Professor
Computing Science
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Adam White

Assistant Professor
Computing Science

앨버타 대학교 정보

UAlberta is considered among the world’s leading public research- and teaching-intensive universities. As one of Canada’s top universities, we’re known for excellence across the humanities, sciences, creative arts, business, engineering and health sciences....

Alberta Machine Intelligence Institute 정보

The Alberta Machine Intelligence Institute (Amii) is home to some of the world’s top talent in machine intelligence. We’re an Alberta-based research institute that pushes the bounds of academic knowledge and guides business understanding of artificial intelligence and machine learning....

강화 학습 전문 분야 정보

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....
강화 학습

자주 묻는 질문

  • 강좌에 등록하면 바로 모든 비디오, 테스트 및 프로그래밍 과제(해당하는 경우)에 접근할 수 있습니다. 상호 첨삭 과제는 이 세션이 시작된 경우에만 제출하고 검토할 수 있습니다. 강좌를 구매하지 않고 살펴보기만 하면 특정 과제에 접근하지 못할 수 있습니다.

  • 강좌를 등록하면 전문 분야의 모든 강좌에 접근할 수 있고 강좌를 완료하면 수료증을 취득할 수 있습니다. 전자 수료증이 성취도 페이지에 추가되며 해당 페이지에서 수료증을 인쇄하거나 LinkedIn 프로필에 수료증을 추가할 수 있습니다. 강좌 내용만 읽고 살펴보려면 해당 강좌를 무료로 청강할 수 있습니다.

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