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Fundamentals of Reinforcement Learning

Coursera · University of Alberta · Updated March 2026

Platform rating

4.5/5

AI Tutor Rating

8.3/10

Duration

1-3 months

Classes

60

Learn the basics of reinforcement learning including Markov Decision Processes, value functions, and dynamic programming.

What you'll get

Understand MDPs, value functions, and policy methods

Implement dynamic programming for RL

Build foundation for advanced RL algorithms

Course Curriculum

6 chapters

Fit

Best for

ML Engineers

Data Scientists

AI Researchers

Deep Learning Practitioners

Not ideal for

Learners seeking only entry-level overviews

Prerequisites & pricing

Prerequisites

Basic programming, probability

Pricing

Subscription

Certification

Certificate

Growth Leverage: Completing this course positions you for roles such as Reinforcement Learning Engineer, Data Scientist, or AI Researcher, opening opportunities in industries like robotics, finance, and gaming where RL techniques are increasingly in demand. It also lays a solid foundation for obtaining advanced certifications in machine learning.

Skills Value: Employers pay a premium for skills in reinforcement learning as they enable the development of intelligent systems that optimize decision-making processes. With an average salary premium of 10-20% over traditional data roles, you can address complex challenges in automation, predictive analytics, and personalized recommendations.

Reinforcement Learning

MDP

Dynamic Programming

Value Functions

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Third methodology is reinforcement learning. It focuses on training models to make decisions through trial and error, receiving feedback from the environment ...

The model generates multiple candidate actions and deep reinforcement learning, RL is used to optimize a policy that selects actions from among the candidates.

Master reinforcement learning from University of Alberta covering MDPs, value functions, policy methods, and deep RL.