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Reinforcement Learning Specialization

Coursera · University of Alberta · Updated March 2026

Platform rating

4.5/5

AI Tutor Rating

8.3/10

Duration

3-6 months

Classes

150

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

What you'll get

Understand MDPs, value functions, and policy methods

Implement deep reinforcement learning algorithms

Train agents for game and simulation environments

Course Curriculum

12 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

Python, probability, linear algebra

Pricing

Subscription

Certification

Certificate

Growth Leverage: Completing the Reinforcement Learning Specialization can propel your career into roles such as Machine Learning Engineer, AI Research Scientist, or Robotics Engineer. It also positions you to pursue advanced certifications like Deep Learning Specialization, enhancing your expertise in AI applications across various industries.

Skills Value: The skills gained allow you to develop intelligent agents for complex environments, which are highly sought after in industries like gaming and finance, where machine learning engineers can earn salaries ranging from $110,000 to $150,000, reflecting the high market demand for expertise in deep reinforcement learning.

Reinforcement Learning

MDP

Deep RL

Policy Gradient

Q-Learning

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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.

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