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 forma parte de Programa especializado: Aprendizaje por refuerzo
48,903 ya inscrito
ofrecido por
Universidad de Alberta
Alberta Machine Intelligence Institute
Programa especializado: Aprendizaje por refuerzoUniversidad de AlbertaAcerca de este Curso
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Resultados profesionales del estudiante
14%
comenzó una nueva carrera después de completar estos cursos
Certificado para compartir
Obtén un certificado al finalizar
100 % en línea
Comienza de inmediato y aprende a tu propio ritmo.
Curso 1 de 4 en
Fechas límite flexibles
Restablece las fechas límite en función de tus horarios.
Nivel intermedio
Probabilities & Expectations, basic linear algebra, basic calculus, Python 3.0 (at least 1 year), implementing algorithms from pseudocode.
Aprox. 15 horas para completar
Inglés (English)
Subtítulos: Árabe (Arabic), Francés (French), Portugués (europeo), Italiano, Vietnamita, Alemán (German), Ruso (Russian), Inglés (English), Español (Spanish)
Qué aprenderás
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 obtendrás
Artificial Intelligence (AI)Machine LearningReinforcement LearningFunction ApproximationIntelligent Systems
Resultados profesionales del estudiante
14%
comenzó una nueva carrera después de completar estos cursos
Certificado para compartir
Obtén un certificado al finalizar
100 % en línea
Comienza de inmediato y aprende a tu propio ritmo.
Curso 1 de 4 en
Fechas límite flexibles
Restablece las fechas límite en función de tus horarios.
Nivel intermedio
Probabilities & Expectations, basic linear algebra, basic calculus, Python 3.0 (at least 1 year), implementing algorithms from pseudocode.
Aprox. 15 horas para completar
Inglés (English)
Subtítulos: Árabe (Arabic), Francés (French), Portugués (europeo), Italiano, Vietnamita, Alemán (German), Ruso (Russian), Inglés (English), Español (Spanish)
ofrecido por
Universidad 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 - Qué aprenderás en este curso
1 hora para completar
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 completar
4 videos (Total 20 minutos), 2 lecturas
4 horas para completar
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 completar
8 videos (Total 46 minutos), 3 lecturas, 2 cuestionarios
3 horas para completar
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 completar
7 videos (Total 36 minutos), 2 lecturas, 2 cuestionarios
3 horas para completar
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 completar
9 videos (Total 56 minutos), 3 lecturas, 2 cuestionarios
4 horas para completar
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 completar
10 videos (Total 72 minutos), 3 lecturas, 2 cuestionarios
Reseñas
Principales reseñas sobre FUNDAMENTALS OF REINFORCEMENT LEARNING
por AR27 de oct. 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 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 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 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!
Acerca de Programa especializado: Aprendizaje por refuerzo
The Reinforcement Learning Specialization consists of 4 courses exploring the power of adaptive learning systems and artificial intelligence (AI).
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