Fundamentals of Reinforcement Learning
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Fundamentals of Reinforcement Learning at Coursera Overview
Duration | 15 hours |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Explore Free Course  |
Credential | Certificate |
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Fundamentals of Reinforcement Learning at Coursera Highlights
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Course 1 of 4 in the Reinforcement Learning Specialization
- Flexible deadlines Reset deadlines in accordance to your schedule.
- Intermediate Level Probabilities & Expectations, basic linear algebra, basic calculus, Python 3.0 (at least 1 year), implementing algorithms from pseudocode.
- Approx. 15 hours to complete
- English Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, German, Russian, English, Spanish
Fundamentals of Reinforcement Learning at Coursera Course details
- 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.
Fundamentals of Reinforcement Learning at Coursera Curriculum
Welcome to the Course!
Specialization Introduction
Course Introduction
Meet your instructors!
Your Specialization Roadmap
Reinforcement Learning Textbook
Read Me: Pre-requisites and Learning Objectives
Sequential Decision Making with Evaluative Feedback
Learning Action Values
Estimating Action Values Incrementally
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
Module 1 Learning Objectives
Weekly Reading
Chapter Summary
Sequential Decision-Making
Markov Decision Processes
Markov Decision Processes
Examples of MDPs
The Goal of Reinforcement Learning
Michael Littman: The Reward Hypothesis
Continuing Tasks
Examples of Episodic and Continuing Tasks
Week 2 Summary
Module 2 Learning Objectives
Weekly Reading
MDPs
Value Functions & Bellman Equations
Specifying Policies
Value Functions
Rich Sutton and Andy Barto: A brief History of RL
Bellman Equation Derivation
Why Bellman Equations?
Optimal Policies
Optimal Value Functions
Using Optimal Value Functions to Get Optimal Policies
Week 3 Summary
Module 3 Learning Objectives
Weekly Reading
Chapter Summary
[Practice] Value Functions and Bellman Equations
Value Functions and Bellman Equations
Dynamic Programming
Policy Evaluation vs. Control
Iterative Policy Evaluation
Policy Improvement
Policy Iteration
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
Congratulations!
Module 4 Learning Objectives
Weekly Reading
Chapter Summary
Dynamic Programming
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