Introduction to Reinforcement Learning in Python

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Coursera Project Network
In this Guided Project, you will:

Implement Monte Carlo techniques for RL

Implement Temporal Difference algorithms in Python

Implement Q-learning in Python

Clock2 hours
CloudNo download needed
VideoSplit-screen video
Comment DotsEnglish
LaptopDesktop only

In this project-based course, we will explore Reinforcement Learning in Python. Reinforcement Learning, or RL for short, is different from supervised learning methods in that, rather than being given correct examples by humans, the AI finds the correct answers for itself through a predefined framework of reward signals. In this course, we will discuss theories and concepts that are integral to RL, such as the Multi-Arm Bandit problem and its implications, and how Markov Decision processes can be leveraged to find solutions. Then we will implement code examples in Python of basic Temporal Difference algorithms and Monte Carlo techniques. Finally, we implement an example of Q-learning in Python. I would encourage learners to experiment with the tools and methods discussed in this course. The learner is highly encouraged to experiment beyond the scope of the course. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

Skills you will develop

Machine LearningAritificial IntelligenceReinforcement Learning in Python

Learn step-by-step

In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:

  1. Learn about the Multi-Arm Bandit problem and the exploration vs. exploitation trade-off

  2. Understand Markov Decision Processes

  3. Implement Monte Carlo techniques for RL

  4.  Implement Temporal Difference algorithms in Python

  5. Implement Q-learning in Python

How Guided Projects work

Your workspace is a cloud desktop right in your browser, no download required

In a split-screen video, your instructor guides you step-by-step

Frequently asked questions

Frequently Asked Questions

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