Reinforcement Learning is a subfield of Machine Learning, but is also a general purpose formalism for automated decision-making and AI. This course introduces you to statistical learning techniques where an agent explicitly takes actions and interacts with the world. Understanding the importance and challenges of learning agents that make decisions is of vital importance today, with more and more companies interested in interactive agents and intelligent decision-making.
Ce cours fait partie de la Spécialisation Apprentissage par renforcement
Offert par
Université de l'Alberta
Alberta Machine Intelligence Institute
Spécialisation Apprentissage par renforcementUniversité de l'AlbertaÀ propos de ce cours
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Résultats de carrière des étudiants
25%
ont commencé une nouvelle carrière après avoir terminé ce cours
10%
ont bénéficié d'un avantage concret dans leur carrières grâce à ce cours
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Cours 1 sur 4 dans le
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Niveau intermédiaire
Probabilities & Expectations, basic linear algebra, basic calculus, Python 3.0 (at least 1 year), implementing algorithms from pseudocode.
Approx. 15 heures pour terminer
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Sous-titres : Arabe, Français, Portugais (européen), Chinois (simplifié), Italien, Vietnamien, Coréen, Allemand, Russe, Turc, Anglais, Espagnol
Ce que vous allez apprendre
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
Compétences que vous acquerrez
Artificial Intelligence (AI)Machine LearningReinforcement LearningFunction ApproximationIntelligent Systems
Résultats de carrière des étudiants
25%
ont commencé une nouvelle carrière après avoir terminé ce cours
10%
ont bénéficié d'un avantage concret dans leur carrières grâce à ce cours
Certificat partageable
Obtenez un Certificat lorsque vous terminez
100 % en ligne
Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Cours 1 sur 4 dans le
Dates limites flexibles
Réinitialisez les dates limites selon votre disponibilité.
Niveau intermédiaire
Probabilities & Expectations, basic linear algebra, basic calculus, Python 3.0 (at least 1 year), implementing algorithms from pseudocode.
Approx. 15 heures pour terminer
Anglais
Sous-titres : Arabe, Français, Portugais (européen), Chinois (simplifié), Italien, Vietnamien, Coréen, Allemand, Russe, Turc, Anglais, Espagnol
Offert par
Université de l'Alberta
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
The Alberta Machine Intelligence Institute (Amii) is home to some of the world’s top talent in machine intelligence. We’re an Alberta-based
Programme du cours : ce que vous apprendrez dans ce cours
1 heure pour terminer
Welcome to the Course!
Welcome to: Fundamentals of Reinforcement Learning, the first course in a four-part specialization on Reinforcement Learning brought to you by the University of Alberta, Onlea, and Coursera. In this pre-course module, you'll be introduced to your instructors, get a flavour of what the course has in store for you, and be given an in-depth roadmap to help make your journey through this specialization as smooth as possible.
1 heure pour terminer
4 vidéos (Total 20 min), 2 lectures
4 vidéos
Course Introduction5 min
Meet your instructors!8 min
Your Specialization Roadmap3 min
2 lectures
Reinforcement Learning Textbook10 min
Read Me: Pre-requisites and Learning Objectives10 min
4 heures pour terminer
An Introduction to Sequential Decision-Making
For the first week of this course, you will learn how to understand the exploration-exploitation trade-off in sequential decision-making, implement incremental algorithms for estimating action-values, and compare the strengths and weaknesses to different algorithms for exploration. For this week’s graded assessment, you will implement and test an epsilon-greedy agent.
4 heures pour terminer
8 vidéos (Total 46 min), 3 lectures, 2 quiz
8 vidéos
Learning Action Values4 min
Estimating Action Values Incrementally5 min
What is the trade-off?7 min
Optimistic Initial Values6 min
Upper-Confidence Bound (UCB) Action Selection5 min
Jonathan Langford: Contextual Bandits for Real World Reinforcement Learning8 min
Week 1 Summary3 min
3 lectures
Module 1 Learning Objectives10 min
Weekly Reading30 min
Chapter Summary30 min
1 exercice pour s'entraîner
Sequential Decision-Making45 min
3 heures pour terminer
Markov Decision Processes
When you’re presented with a problem in industry, the first and most important step is to translate that problem into a Markov Decision Process (MDP). The quality of your solution depends heavily on how well you do this translation. This week, you will learn the definition of MDPs, you will understand goal-directed behavior and how this can be obtained from maximizing scalar rewards, and you will also understand the difference between episodic and continuing tasks. For this week’s graded assessment, you will create three example tasks of your own that fit into the MDP framework.
3 heures pour terminer
7 vidéos (Total 36 min), 2 lectures, 2 quiz
7 vidéos
Examples of MDPs4 min
The Goal of Reinforcement Learning3 min
Michael Littman: The Reward Hypothesis12 min
Continuing Tasks5 min
Examples of Episodic and Continuing Tasks3 min
Week 2 Summary1 min
2 lectures
Module 2 Learning Objectives10 min
Weekly Reading30 min
1 exercice pour s'entraîner
MDPs45 min
3 heures pour terminer
Value Functions & Bellman Equations
Once the problem is formulated as an MDP, finding the optimal policy is more efficient when using value functions. This week, you will learn the definition of policies and value functions, as well as Bellman equations, which is the key technology that all of our algorithms will use.
3 heures pour terminer
9 vidéos (Total 56 min), 3 lectures, 2 quiz
9 vidéos
Specifying Policies4 min
Value Functions6 min
Rich Sutton and Andy Barto: A brief History of RL7 min
Bellman Equation Derivation6 min
Why Bellman Equations?5 min
Optimal Policies7 min
Optimal Value Functions5 min
Using Optimal Value Functions to Get Optimal Policies8 min
Week 3 Summary4 min
3 lectures
Module 3 Learning Objectives10 min
Weekly Reading30 min
Chapter Summary13 min
2 exercices pour s'entraîner
[Practice] Value Functions and Bellman Equations45 min
Value Functions and Bellman Equations45 min
4 heures pour terminer
Dynamic Programming
This week, you will learn how to compute value functions and optimal policies, assuming you have the MDP model. You will implement dynamic programming to compute value functions and optimal policies and understand the utility of dynamic programming for industrial applications and problems. Further, you will learn about Generalized Policy Iteration as a common template for constructing algorithms that maximize reward. For this week’s graded assessment, you will implement an efficient dynamic programming agent in a simulated industrial control problem.
4 heures pour terminer
10 vidéos (Total 72 min), 3 lectures, 2 quiz
10 vidéos
Iterative Policy Evaluation8 min
Policy Improvement4 min
Policy Iteration8 min
Flexibility of the Policy Iteration Framework4 min
Efficiency of Dynamic Programming5 min
Warren Powell: Approximate Dynamic Programming for Fleet Management (Short)7 min
Warren Powell: Approximate Dynamic Programming for Fleet Management (Long)21 min
Week 4 Summary2 min
Congratulations!3 min
3 lectures
Module 4 Learning Objectives10 min
Weekly Reading30 min
Chapter Summary30 min
1 exercice pour s'entraîner
Dynamic Programming45 min
Avis
Meilleurs avis pour FUNDAMENTALS OF REINFORCEMENT LEARNING
par AR27 oct. 2020
The video lectures were very short and just a repetition of the book itself. After we studied the book, the lectures didn't have anything new for us. They should have been different and more hands-on.
par AM23 août 2020
Don't think it would be unreasonable to have more demanding coding assignments where all functions are made from scratch (though the function names and some comments might be provided as an outline.
par AT6 juil. 2020
An excellent introduction to Reinforcement Learning, accompanied by a well-organized & informative handbook. I definitely recommend this course to have a strong foundation in Reinforcement Learning.
par NH7 avr. 2020
This course is one of the best I've learned so far in coursera. The explanations are clear and concise enough. It took a while for me to understand Bellman equation but when I did, it felt amazing!
À propos du Spécialisation Apprentissage par renforcement
The Reinforcement Learning Specialization consists of 4 courses exploring the power of adaptive learning systems and artificial intelligence (AI).
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