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Fundamentals of Reinforcement Learning
  • Level Professional
  • Duration 15 hours
  • Course by University of Alberta
  • Offered by

About

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.

Modules

Course Introduction

    • 4 Videos
    • 2 Readings
    • 1 Discussion
Show info about module content

1 Discussions

  • Meet and Greet!

4 Videos

  • Specialization Introduction
  • Course Introduction
  • Meet your instructors!
  • Your Specialization Roadmap

2 Readings

  • Reinforcement Learning Textbook
  • Read Me: Pre-requisites and Learning Objectives

The K-Armed Bandit Problem

    • 1 Videos
    • 2 Readings
    • 1 Discussion
    • 1 Plugin
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1 Discussions

  • Compare bandits to supervised learning

1 Videos

  • Sequential Decision Making with Evaluative Feedback

2 Readings

  • Module 1 Learning Objectives
  • Weekly Reading

What to Learn? Estimating Action Values

    • 2 Videos
    • 1 Plugin
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2 Videos

  • Learning Action Values
  • Estimating Action Values Incrementally

Exploration vs. Exploitation Tradeoff

    • 5 Videos
    • 1 Readings
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5 Videos

  • What is the trade-off?
  • Optimistic Initial Values
  • Upper-Confidence Bound (UCB) Action Selection
  • Jonathan Langford: Contextual Bandits for Real World Reinforcement Learning
  • Week 1 Summary

1 Readings

  • Chapter Summary

Weekly Assessment

    • 1 Programming
    • 1 Assignment
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1 Programming

  • Bandits and Exploration/Exploitation

1 Assignment

  • Sequential Decision-Making

Introduction to Markov Decision Processes

    • 2 Videos
    • 2 Readings
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2 Videos

  • Markov Decision Processes
  • Examples of MDPs

2 Readings

  • Module 2 Learning Objectives
  • Weekly Reading

Goal of Reinforcement Learning

    • 2 Videos
    • 1 Discussion
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1 Discussions

  • Is the reward hypothesis sufficient?

2 Videos

  • The Goal of Reinforcement Learning
  • Michael Littman: The Reward Hypothesis

Continuing Tasks

    • 3 Videos
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3 Videos

  • Continuing Tasks
  • Examples of Episodic and Continuing Tasks
  • Week 2 Summary

Weekly Assesment

    • 1 PeerReview
    • 1 Assignment
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1 Assignment

  • MDPs

1 Peer Review

  • Graded Assignment: Describe Three MDPs

Policies and Value Functions

    • 3 Videos
    • 2 Readings
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3 Videos

  • Specifying Policies
  • Value Functions
  • Rich Sutton and Andy Barto: A brief History of RL

2 Readings

  • Module 3 Learning Objectives
  • Weekly Reading

Bellman Equations

    • 2 Videos
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2 Videos

  • Bellman Equation Derivation
  • Why Bellman Equations?

Optimality (Optimal Policies & Value Functions)

    • 4 Videos
    • 1 Readings
    • 1 Discussion
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1 Discussions

  • Check-in

4 Videos

  • Optimal Policies
  • Optimal Value Functions
  • Using Optimal Value Functions to Get Optimal Policies
  • Week 3 Summary

1 Readings

  • Chapter Summary

Weekly Assessment

    • 2 Assignment
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2 Assignment

  • [Practice] Value Functions and Bellman Equations
  • [Graded] Value Functions and Bellman Equations

Policy Evaluation (Prediction)

    • 2 Videos
    • 2 Readings
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2 Videos

  • Policy Evaluation vs. Control
  • Iterative Policy Evaluation

2 Readings

  • Module 4 Learning Objectives
  • Weekly Reading

Policy Iteration (Control)

    • 2 Videos
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2 Videos

  • Policy Improvement
  • Policy Iteration

Generalized Policy Iteration

    • 5 Videos
    • 1 Readings
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5 Videos

  • Flexibility of the Policy Iteration Framework
  • Efficiency of Dynamic Programming
  • Warren Powell: Approximate Dynamic Programming for Fleet Management (Short)
  • Warren Powell: Approximate Dynamic Programming for Fleet Management (Long)
  • Week 4 Summary

1 Readings

  • Chapter Summary

Weekly Assessment

    • 1 Discussion
    • 1 Programming
    • 1 Assignment
Show info about module content

1 Programming

  • Optimal Policies with Dynamic Programming

1 Assignment

  • Dynamic Programming

1 Discussions

  • Where can you use dynamic programming?

Course Wrap-up

    • 1 Videos
Show info about module content

1 Videos

  • Congratulations!

Auto Summary

"Fundamentals of Reinforcement Learning" is a professional-level course offered by Coursera, focusing on key concepts and algorithms in the domain of Data Science & AI. Led by expert instructors, it covers Markov Decision Processes, exploration methods, value functions, and dynamic programming. Ideal for those interested in automated decision-making and AI, the 900-minute course equips learners to apply reinforcement learning to real-world problems. Available via a Starter subscription, it's perfect for professionals seeking to enhance their skills in interactive agents and intelligent decision-making.

Martha White
Instructors

Martha White

Adam White
Instructors

Adam White