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.
Este curso faz parte do Programa de cursos integrados Reforço de aprendizagem
oferecido por
Universidade de AlbertaUniversidade de Alberta
Alberta Machine Intelligence Institute
Programa de cursos integrados Reforço de aprendizagemUniversidade de AlbertaUniversidade de AlbertaInformações sobre o curso
170,292 visualizações recentes
Resultados de carreira do aprendiz
25%
comecei uma nova carreira após concluir estes cursos
11%
consegui um benefício significativo de carreira com este curso
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Curso 1 de 4 no
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Nível intermediário
Probabilities & Expectations, basic linear algebra, basic calculus, Python 3.0 (at least 1 year), implementing algorithms from pseudocode.
Aprox. 15 horas para completar
Inglês
Legendas: Francês, Português (Brasil), Russo, Inglês, Espanhol
O que você vai aprender
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
Habilidades que você terá
Artificial Intelligence (AI)Machine LearningReinforcement LearningFunction ApproximationIntelligent Systems
Resultados de carreira do aprendiz
25%
comecei uma nova carreira após concluir estes cursos
11%
consegui um benefício significativo de carreira com este curso
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Curso 1 de 4 no
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Nível intermediário
Probabilities & Expectations, basic linear algebra, basic calculus, Python 3.0 (at least 1 year), implementing algorithms from pseudocode.
Aprox. 15 horas para completar
Inglês
Legendas: Francês, Português (Brasil), Russo, Inglês, Espanhol
oferecido por
Universidade de AlbertaUniversidade de 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
Programa - O que você aprenderá com este curso
1 hora para concluir
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 hora para concluir
4 vídeos (Total 20 mín.), 2 leituras
4 videos
Course Introduction5min
Meet your instructors!8min
Your Specialization Roadmap3min
2 leituras
Reinforcement Learning Textbook10min
Read Me: Pre-requisites and Learning Objectives10min
4 horas para concluir
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 horas para concluir
8 vídeos (Total 46 mín.), 3 leituras, 2 testes
8 videos
Learning Action Values4min
Estimating Action Values Incrementally5min
What is the trade-off?7min
Optimistic Initial Values6min
Upper-Confidence Bound (UCB) Action Selection5min
Jonathan Langford: Contextual Bandits for Real World Reinforcement Learning8min
Week 1 Summary3min
3 leituras
Module 1 Learning Objectives10min
Weekly Reading30min
Chapter Summary30min
1 exercício prático
Sequential Decision-Making45min
3 horas para concluir
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 horas para concluir
7 vídeos (Total 36 mín.), 2 leituras, 2 testes
7 videos
Examples of MDPs4min
The Goal of Reinforcement Learning3min
Michael Littman: The Reward Hypothesis12min
Continuing Tasks5min
Examples of Episodic and Continuing Tasks3min
Week 2 Summary1min
2 leituras
Module 2 Learning Objectives10min
Weekly Reading30min
1 exercício prático
MDPs45min
3 horas para concluir
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 horas para concluir
9 vídeos (Total 56 mín.), 3 leituras, 2 testes
9 videos
Value Functions6min
Rich Sutton and Andy Barto: A brief History of RL7min
Bellman Equation Derivation6min
Why Bellman Equations?5min
Optimal Policies7min
Optimal Value Functions5min
Using Optimal Value Functions to Get Optimal Policies8min
Week 3 Summary4min
3 leituras
Module 3 Learning Objectives10min
Weekly Reading30min
Chapter Summary13min
2 exercícios práticos
[Practice] Value Functions and Bellman Equations45min
Value Functions and Bellman Equations45min
4 horas para concluir
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 horas para concluir
10 vídeos (Total 72 mín.), 3 leituras, 2 testes
10 videos
Iterative Policy Evaluation8min
Policy Improvement4min
Policy Iteration8min
Flexibility of the Policy Iteration Framework4min
Efficiency of Dynamic Programming5min
Warren Powell: Approximate Dynamic Programming for Fleet Management (Short)7min
Warren Powell: Approximate Dynamic Programming for Fleet Management (Long)21min
Week 4 Summary2min
Congratulations!3min
3 leituras
Module 4 Learning Objectives10min
Weekly Reading30min
Chapter Summary30min
1 exercício prático
Dynamic Programming45min
Avaliações
Principais avaliações do FUNDAMENTALS OF REINFORCEMENT LEARNING
por AR27 de Out de 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.
por AM23 de Ago de 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.
por AT6 de Jul de 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.
por NH7 de Abr de 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!
Sobre Programa de cursos integrados Reforço de aprendizagem
The Reinforcement Learning Specialization consists of 4 courses exploring the power of adaptive learning systems and artificial intelligence (AI).
Perguntas Frequentes – FAQ
Quando terei acesso às palestras e às tarefas?
O que recebo ao me inscrever nesta Especialização?
Is financial aid available?
Ganharei créditos universitários por concluir o curso?
Mais dúvidas? Visite o Central de Ajuda ao Aprendiz.
