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This course is part of Reinforcement Learning Specialization
Instructors: Martha White
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Intermediate level
Probabilities & Expectations, basic linear algebra, basic calculus, Python 3.0 (at least 1 year), implementing algorithms from pseudocode.
(2,814 reviews)
Recommended experience
Intermediate level
Probabilities & Expectations, basic linear algebra, basic calculus, Python 3.0 (at least 1 year), implementing algorithms from pseudocode.
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
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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.
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.
4 videos2 readings1 discussion prompt
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.
8 videos3 readings1 assignment1 programming assignment1 discussion prompt2 plugins
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.
7 videos2 readings1 assignment1 peer review1 discussion prompt
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.
9 videos3 readings2 assignments1 discussion prompt
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.
10 videos3 readings1 assignment1 programming assignment1 discussion prompt
We asked all learners to give feedback on our instructors based on the quality of their teaching style.
The University of Alberta is considered among the world’s leading public research- and teaching-intensive universities, known for excellence across the humanities, sciences, creative arts, business, engineering and health sciences. As one of Canada’s top universities, we are investing in purpose-built online post-secondary education—rooted in innovative digital pedagogies, world-class faculty, exceptional design, and a championed student experience.
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Reviewed on Jul 1, 2021
This course is great for people who are just starting out. The programming assignments are really great and practically introduce you to the basic concepts of reinforcement learning.
Reviewed on Apr 11, 2024
The concepts may sound confusing in the beginning, but as you go forward you find it interesting and understanding. I suggest you completely read the reading assignments before watching the videos.
Reviewed on Dec 3, 2020
This course was super helpful. I had tried a couple other online introductions to RL, but this was the only one where I could really engage and learn the material effectively. Would recommend!
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