Course Summary
This course covers the fundamentals of Reinforcement Learning, a type of machine learning where an agent learns to make decisions in an environment by trial and error. You will learn the basic concepts, algorithms, and applications of Reinforcement Learning.Key Learning Points
- Understand the basic concepts of Reinforcement Learning
- Learn about different algorithms used in Reinforcement Learning
- Apply Reinforcement Learning to real-world problems
- Understand the challenges and limitations of Reinforcement Learning
Related Topics for further study
Learning Outcomes
- Understand the basics of Reinforcement Learning and its applications
- Implement different Reinforcement Learning algorithms
- Apply Reinforcement Learning to solve real-world problems
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of Python programming language
- Basic understanding of Machine Learning concepts
Course Difficulty Level
IntermediateCourse Format
- Online Self-paced Course
- Video Lectures
- Assignments and Quizzes
Similar Courses
- Deep Reinforcement Learning
- Applied Data Science with Python
- Artificial Intelligence
Related Education Paths
Notable People in This Field
- Professor
- Professor
Related Books
Description
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.
Knowledge
- 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
Outline
- Welcome to the Course!
- Specialization Introduction
- Course Introduction
- Meet your instructors!
- Your Specialization Roadmap
- Reinforcement Learning Textbook
- Read Me: Pre-requisites and Learning Objectives
- An Introduction to Sequential Decision-Making
- 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
Summary of User Reviews
Discover the fundamentals of reinforcement learning with this highly rated Coursera course. Users rave about the engaging and informative content, with many citing the hands-on exercises as a standout aspect. However, some users have noted that the course can be challenging and requires a solid background in math and programming.Pros from User Reviews
- Engaging and informative content
- Well-structured course materials
- Great introduction to reinforcement learning
Cons from User Reviews
- Challenging for those without a strong math and programming background
- Some lectures can be difficult to follow
- Limited interaction with instructors
- Not enough practice problems or quizzes
Martha White
4.8 Raiting
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