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
ofrecido por
Universidad de Alberta
Alberta Machine Intelligence Institute
Programa Especializado - Aprendizaje por refuerzoUniversidad de AlbertaAcerca de este Curso
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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. 21 horas para completar
Inglés (English)
Subtítulos: 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 LearningReinforcement LearningFunction ApproximationIntelligent Systems
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. 21 horas para completar
Inglés (English)
Subtítulos: Inglés (English)
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 videos
Course Introduction5m
Meet your instructors!8m
Your Specialization Roadmap3m
2 lecturas
Reinforcement Learning Textbook10m
Read Me: Pre-requisites and Learning Objectives10m
7 horas para completar
The K-Armed Bandit Problem
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.
7 horas para completar
8 videos (Total 46 minutos), 3 lecturas, 2 cuestionarios
8 videos
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 2 Learning Objectives10m
Weekly Reading30m
Chapter Summary30m
1 ejercicio de práctica
Exploration/Exploitation45m
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
7 videos
Examples of MDPs4m
The Goal of Reinforcement Learning3m
Michael Littman: The Reward Hypothesis12m
Continuing Tasks5m
Examples of Episodic and Continuing Tasks3m
Week 2 Summary1m
2 lecturas
Module 3 Learning Objectives10m
Weekly Reading30m
1 ejercicio de práctica
MDPs45m
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
9 videos
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 4 Learning Objectives10m
Weekly Reading30m
Chapter Summary13m
2 ejercicios de práctica
Value Functions and Bellman Equations45m
Value Functions and Bellman Equations45m
7 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.
7 horas para completar
10 videos (Total 72 minutos), 3 lecturas, 2 cuestionarios
10 videos
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 5 Learning Objectives10m
Weekly Reading30m
Chapter Summary30m
1 ejercicio de práctica
Dynamic Programming45m
Revisiones
Principales revisiones sobre FUNDAMENTALS OF 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!
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 AB6 de sep. de 2019
Concepts are bit hard, but it is nice if you undersand it well, espically the bellman and dynamic programming. Sometimes, visualizing the problem is hard, so need to thoroghly get prepared.
por RD25 de abr. de 2020
I was so confused about the fundamental concepts, but doing this course has given me a solid foundation of RL. This is a must-do course if you are starting with Reinforcement Learning.
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?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
- The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
- The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
¿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.
¿Cuál es la política de reembolsos?
Si estás suscrito, obtienes una prueba gratis de 7 días, que podrás cancelar cuando desees sin ningún tipo de penalidad. Una vez transcurrido ese tiempo, no realizamos reembolsos. No obstante, puedes cancelar tu suscripción cuando quieras. Consulta nuestra política completa de reembolsos.
¿Hay ayuda económica disponible?
Sí, Coursera ofrece ayuda económica a los estudiantes que no pueden pagar la tarifa. Solicítala haciendo clic en el enlace de Ayuda económica que está debajo del botón “Inscribirse” a la izquierda. Se te pedirá que completes una solicitud. Recibirás una notificación en caso de que se apruebe. Deberás completar este paso para cada uno de los cursos que forman parte del Programa especializado, incluido el proyecto final. Obtén más información.
¿Recibiré crédito universitario por completar el Curso?
Este Curso no otorga crédito universitario, pero algunas universidades pueden aceptar los Certificados del curso para obtener crédito. Consulta con tu institución para obtener más información. Los Títulos en línea y los Certificados Mastertrack™ de Coursera brindan la oportunidad de obtener créditos universitarios.
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