Este curso forma parte de Programa especializado: Aprendizaje por refuerzo

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92 %

Martha White +1 instructores más

71.810 ya inscrito

Ofrecido Por

Universidad de Alberta

Alberta Machine Intelligence Institute

Programa especializado: Aprendizaje por refuerzoUniversidad de Alberta

Acerca de este Curso

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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. 
This course introduces you to the fundamentals of Reinforcement Learning. When you finish this course, you will:
- 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

This course teaches you the key concepts of Reinforcement Learning, underlying classic and modern algorithms in RL. After completing this course, you will be able to start using RL for real problems, where you have or can specify the MDP. 

This is the first course of the Reinforcement Learning Specialization.

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Curso 1 de 4 en

Programa especializado: Aprendizaje por refuerzo

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)

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 Learning
Reinforcement Learning
Function Approximation
Intelligent Systems

Fechas límite flexibles

Restablece las fechas límite en función de tus horarios.

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

Programa especializado: Aprendizaje por refuerzo

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)

Instructores

Calificación del instructor

4,7/5 (715 calificaciones)

Martha White

Adam White

Ofrecido por

Universidad de Alberta

Alberta Machine Intelligence Institute

Programa - Qué aprenderás en este curso

Calificación del contenido92%(17,608 calificaciones)

Semana

Semana 1

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

4 horas para completar

8 videos (Total 46 minutos), 3 lecturas, 2 cuestionarios

8 videos

Sequential Decision Making with Evaluative Feedback5m

Learning Action Values4m

Estimating Action Values Incrementally5m

What is the trade-off?7m

Optimistic Initial Values6m

Upper-Confidence Bound (UCB) Action Selection5m

Jonathan Langford: Contextual Bandits for Real World Reinforcement Learning8m

Week 1 Summary3m

3 lecturas

Module 1 Learning Objectives10m

Weekly Reading30m

Chapter Summary30m

1 ejercicio de práctica

Sequential Decision-Making45m

Semana

Semana 2

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

Semana

Semana 3

3 horas para completar

Value Functions & Bellman Equations

3 horas para completar

9 videos (Total 56 minutos), 3 lecturas, 2 cuestionarios

9 videos

Specifying Policies4m

Value Functions6m

Rich Sutton and Andy Barto: A brief History of RL7m

Bellman Equation Derivation6m

Why Bellman Equations?5m

Optimal Policies7m

Optimal Value Functions5m

Using Optimal Value Functions to Get Optimal Policies8m

Week 3 Summary4m

3 lecturas

Module 3 Learning Objectives10m

Weekly Reading30m

Chapter Summary13m

2 ejercicios de práctica

[Practice] Value Functions and Bellman Equations45m

[Graded] Value Functions and Bellman Equations45m

Semana

Semana 4

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

10 videos

Policy Evaluation vs. Control4m

Iterative Policy Evaluation8m

Policy Improvement4m

Policy Iteration8m

Flexibility of the Policy Iteration Framework4m

Efficiency of Dynamic Programming5m

Warren Powell: Approximate Dynamic Programming for Fleet Management (Short)7m

Warren Powell: Approximate Dynamic Programming for Fleet Management (Long)21m

Week 4 Summary2m

Congratulations!3m

3 lecturas

Module 4 Learning Objectives10m

Weekly Reading30m

Chapter Summary30m

1 ejercicio de práctica

Dynamic Programming45m

Reseñas

4.8

605 reseña

5 stars
81,99 %
4 stars
14,66 %
3 stars
2,35 %
2 stars
0,31 %
1 star
0,67 %

Principales reseñas sobre FUNDAMENTALS OF REINFORCEMENT LEARNING

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 PV9 de nov. de 2019

I understood all the necessary concepts of RL. I've been working on RL for some time now, but thanks to this course, now I have more basic knowledge about RL and can't wait to watch other courses

por U2 de ene. de 2021

The book is essential reading. It took me longer than the estimates to do the reading and the programming assignments. I would have liked more gridworld examples to get a faster hang of it.

por CC5 de may. de 2021

Fantastic course! I have been interested in Reinforcement Learning for a long time and this has been the best introduction I have found so far. It gave me the foundations on the field.

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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).

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.

Preguntas Frecuentes

¿Cuándo podré acceder a las lecciones y tareas?
El acceso a las clases y las asignaciones depende del tipo de inscripción que tengas. Si tomas un curso en modo de oyente, verás la mayoría de los materiales del curso en forma gratuita. Para acceder a asignaciones calificadas y obtener un certificado, deberás comprar la experiencia de Certificado, ya sea durante o después de participar como oyente. Si no ves la opción de oyente:
es posible que el curso no ofrezca la opción de participar como oyente. En cambio, puedes intentar con una Prueba gratis o postularte para recibir ayuda económica.
Es posible que el curso ofrezca la opción 'Curso completo, sin certificado'. Esta opción te permite ver todos los materiales del curso, enviar las evaluaciones requeridas y obtener una calificación final. También significa que no podrás comprar una experiencia de Certificado.
¿Qué recibiré si me suscribo a este Programa especializado?
Cuando te inscribes en un curso, obtienes acceso a todos los cursos que forman parte del Programa especializado y te darán un Certificado cuando completes el trabajo. Se añadirá tu Certificado electrónico a la página Logros. Desde allí, puedes imprimir tu Certificado o añadirlo a tu perfil de LinkedIn. Si solo quieres leer y visualizar el contenido del curso, puedes auditar el curso sin costo.
¿Hay ayuda económica disponible?
Sí. En ciertos programas de aprendizaje, puedes postularte para recibir ayuda económica o una beca en caso de no poder costear los gastos de la tarifa de inscripción. Si hay ayuda económica o becas disponibles para tu selección de programa de aprendizaje, verás un enlace para postularte en la página de descripción.

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Fundamentals of Reinforcement Learning

Acerca de este Curso

Qué aprenderás

Habilidades que obtendrás