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Reinforcement Learning

Charles Isbell, Michael Littman, and Chris Pryby

Take Udacity's online Reinforcement Learning course and study machine learning at a deeper level. Become a participant in the reinforcement learning research community.

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What's inside

Syllabus

Introduction to Reinforcement Learning

1. Smoov & Curly's Bogus Journey

2. Reinforcement Learning Basics

3. TD and Friends

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Read about what's good

what should give you pause

and possible dealbreakers

Taught by experts like Michael Littman and Chris Pryby, who are well-known for their research and contributions to the field of reinforcement learning

Offers a comprehensive and structured approach to reinforcement learning concepts

Provides a strong foundation for understanding the basics of reinforcement learning, making it suitable for beginners in the field

Includes hands-on labs and resources, enabling learners to apply reinforcement learning concepts in practice

May be a good fit for individuals with a background in machine learning or related fields who are looking to enhance their understanding of reinforcement learning

Assumes some familiarity with basic machine learning concepts, which may be a barrier for those completely new to the field

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Reviews summary

Deep theoretical dive into reinforcement learning

According to learners, this course offers a strong theoretical foundation in Reinforcement Learning, covering core concepts like MDPs, TD learning, and exploration techniques in depth. Students appreciate the clear explanations provided in the lectures. However, many note that the assignments can be quite challenging and require a solid prerequisite background in linear algebra, calculus, and programming. Some reviewers feel the content, while foundational, might not cover the very latest research advancements, focusing more on established principles. Overall, it's seen as a valuable course for building a deep understanding, though it demands significant effort.

Covers fundamentals, less bleeding edge.

"It's a great course for the basics, but don't expect the very latest research trends."

"Provides a solid foundation, but felt a bit dated in parts compared to recent papers."

"Good overview of classic RL methods."

"The course gives you the fundamentals you need to read more advanced material."

Instructor explains concepts well.

"The instructor does a great job explaining complicated topics."

"Lectures were clear and easy to follow."

"I found the explanations of dynamic programming particularly helpful."

"The way the concepts were presented made sense."

Provides a deep foundation in RL theory.

"The course provides a really strong foundation in the theoretical aspects of reinforcement learning."

"I gained a deep understanding of the core algorithms and concepts."

"The lectures explain complex theoretical ideas with clarity."

"This course excels at building the theoretical background needed for RL."

Requires strong math and coding skills.

"You really need a solid background in linear algebra and calculus to follow along fully."

"Make sure your programming skills are strong before starting this."

"I recommend brushing up on probabilities and optimization first."

"Assumes a certain level of mathematical maturity."

Homeworks are challenging, require effort.

"The assignments were quite difficult and time-consuming."

"Be prepared to spend a lot of time on the homework problems."

"I struggled with some of the practical assignments, they were very challenging."

"Needed to revisit prerequisites heavily for the problem sets."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Reinforcement Learning with these activities:

Connect with experienced practitioners in the field

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Establish connections with individuals working in reinforcement learning to gain insights and guidance.

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Attend industry events and meetups focused on reinforcement learning.
Reach out to researchers or professionals in your network via LinkedIn or email.
Request mentorship or guidance on your reinforcement learning journey.

Explore reinforcement learning tutorials on Coursera

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Gain a foundational understanding of reinforcement learning principles through interactive tutorials on Coursera.

Browse courses on Reinforcement Learning

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Identify suitable Coursera tutorials on reinforcement learning.
Follow the tutorials, completing exercises and assignments.
Seek clarification on concepts or tasks in the discussion forums.

Review partial order sets and functions

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Reinforce concepts related to partially ordered sets and functions, which are fundamental for understanding reinforcement learning.

Browse courses on Functions

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Revisit the definitions and properties of partial ordered sets and functions.
Practice identifying partial ordered sets and determining their properties.
Review examples of functions within partial ordered sets and analyze their behavior.

Four other activities

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Solve dynamic programming problems

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Strengthen problem-solving skills in dynamic programming, a technique closely related to reinforcement learning.

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Review the concepts and algorithms of dynamic programming.
Practice solving a variety of dynamic programming problems.
Analyze the solutions and identify patterns and strategies.

Develop a simple reinforcement learning game

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Apply reinforcement learning concepts by creating a simple game that demonstrates the principles of learning and reward-based behavior.

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Design a simple game environment with clear rules and actions.
Implement a reinforcement learning agent that interacts with the game environment.
Train the agent using reinforcement learning algorithms and evaluate its performance.
Analyze the results and make adjustments to the agent or environment as needed.

Participate in Kaggle competitions on reinforcement learning

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Engage in real-world reinforcement learning challenges by participating in Kaggle competitions, testing your skills and learning from others.

Browse courses on Kaggle Competitions

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Identify relevant Kaggle competitions focused on reinforcement learning.
Build or modify reinforcement learning models to address the competition tasks.
Submit your solutions and track your progress against other participants.
Analyze the results and seek feedback to improve your approach.

Develop a reinforcement learning solution for a business problem

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Apply your reinforcement learning knowledge to a practical business problem, showcasing your ability to bridge theory and practice.

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Identify a business problem that can be addressed using reinforcement learning.
Design and implement a reinforcement learning solution to solve the problem.
Evaluate the performance of your solution and make improvements as needed.
Present your solution to stakeholders, including a demonstration and analysis.

Career center

Learners who complete Reinforcement Learning will develop knowledge and skills that may be useful to these careers:

Machine Learning Engineer

Machine Learning Engineers use their expertise in machine learning algorithms to design and build machine learning systems. As a Machine Learning Engineer, you will participate in developing and implementing machine learning solutions to real-world problems. Reinforcement Learning is an important branch of machine learning that is used to train agents to take actions in an environment so as to maximize rewards. This course will help you build a foundation in reinforcement learning and prepare you for a career as a Machine Learning Engineer.

See salaries and explore the career path for Machine Learning Engineer

Deep Learning Engineer

Deep Learning Engineers use their expertise in deep learning algorithms to design and build deep learning systems. As a Deep Learning Engineer, you will participate in developing and implementing deep learning solutions to real-world problems. Reinforcement Learning is an important branch of deep learning that is used to train agents to take actions in an environment so as to maximize rewards. This course will help you build a foundation in reinforcement learning and prepare you for a career as a Deep Learning Engineer.

See salaries and explore the career path for Deep Learning Engineer

Data Scientist

Data Scientists use their expertise in data science to solve real-world problems. As a Data Scientist, you will participate in collecting, analyzing, and interpreting data to solve problems and make decisions. Reinforcement Learning is an important branch of data science that is used to train agents to take actions in an environment so as to maximize rewards. This course will help you build a foundation in reinforcement learning and prepare you for a career as a Data Scientist.

See salaries and explore the career path for Data Scientist

Artificial Intelligence Engineer

Artificial Intelligence Engineers use their expertise in artificial intelligence to design and build artificial intelligence systems. As an Artificial Intelligence Engineer, you will participate in developing and implementing artificial intelligence solutions to real-world problems. Reinforcement Learning is an important branch of artificial intelligence that is used to train agents to take actions in an environment so as to maximize rewards. This course will help you build a foundation in reinforcement learning and prepare you for a career as an Artificial Intelligence Engineer.

See salaries and explore the career path for Artificial Intelligence Engineer

Machine Learning Researcher

Machine Learning Researchers use their expertise in machine learning to conduct research on new machine learning algorithms and techniques. As a Machine Learning Researcher, you will participate in developing new machine learning algorithms and techniques to solve real-world problems. Reinforcement Learning is an important branch of machine learning that is used to train agents to take actions in an environment so as to maximize rewards. This course will help you build a foundation in reinforcement learning and prepare you for a career as a Machine Learning Researcher.

See salaries and explore the career path for Machine Learning Researcher

Deep Learning Researcher

Deep Learning Researchers use their expertise in deep learning to conduct research on new deep learning algorithms and techniques. As a Deep Learning Researcher, you will participate in developing new deep learning algorithms and techniques to solve real-world problems. Reinforcement Learning is an important branch of deep learning that is used to train agents to take actions in an environment so as to maximize rewards. This course will help you build a foundation in reinforcement learning and prepare you for a career as a Deep Learning Researcher.

See salaries and explore the career path for Deep Learning Researcher

Data Science Researcher

Data Science Researchers use their expertise in data science to conduct research on new data science algorithms and techniques. As a Data Science Researcher, you will participate in developing new data science algorithms and techniques to solve real-world problems. Reinforcement Learning is an important branch of data science that is used to train agents to take actions in an environment so as to maximize rewards. This course will help you build a foundation in reinforcement learning and prepare you for a career as a Data Science Researcher.

See salaries and explore the career path for Data Science Researcher

Artificial Intelligence Researcher

Artificial Intelligence Researchers use their expertise in artificial intelligence to conduct research on new artificial intelligence algorithms and techniques. As an Artificial Intelligence Researcher, you will participate in developing new artificial intelligence algorithms and techniques to solve real-world problems. Reinforcement Learning is an important branch of artificial intelligence that is used to train agents to take actions in an environment so as to maximize rewards. This course will help you build a foundation in reinforcement learning and prepare you for a career as an Artificial Intelligence Researcher.

See salaries and explore the career path for Artificial Intelligence Researcher

Software Engineer

Software Engineers use their expertise in software development to design and build software systems. As a Software Engineer, you will participate in developing and implementing software solutions to real-world problems. Reinforcement Learning may be used to develop software systems that can learn and adapt to their environment. This course may help you build a foundation in reinforcement learning and prepare you for a career as a Software Engineer.

See salaries and explore the career path for Software Engineer

Data Analyst

Data Analysts use their expertise in data analysis to collect, analyze, and interpret data to solve problems and make decisions. Reinforcement Learning may be used to develop data analysis tools that can learn and adapt to new data. This course may help you build a foundation in reinforcement learning and prepare you for a career as a Data Analyst.

See salaries and explore the career path for Data Analyst

Operations Research Analyst

Operations Research Analysts use their expertise in operations research to solve problems in a variety of industries. Reinforcement Learning may be used to develop operations research models that can learn and adapt to new data. This course may help you build a foundation in reinforcement learning and prepare you for a career as an Operations Research Analyst.

See salaries and explore the career path for Operations Research Analyst

Financial Analyst

Financial Analysts use their expertise in finance to analyze financial data and make investment decisions. Reinforcement Learning may be used to develop financial analysis tools that can learn and adapt to new data. This course may help you build a foundation in reinforcement learning and prepare you for a career as a Financial Analyst.

See salaries and explore the career path for Financial Analyst

Management Consultant

Management Consultants use their expertise in business to help organizations solve problems and improve their performance. Reinforcement Learning may be used to develop management consulting tools that can learn and adapt to new data. This course may help you build a foundation in reinforcement learning and prepare you for a career as a Management Consultant.

See salaries and explore the career path for Management Consultant

Product Manager

Product Managers use their expertise in product development to design and build products that meet the needs of their customers. Reinforcement Learning may be used to develop product management tools that can learn and adapt to new data. This course may help you build a foundation in reinforcement learning and prepare you for a career as a Product Manager.

See salaries and explore the career path for Product Manager

Marketing Manager

Marketing Managers use their expertise in marketing to develop and implement marketing campaigns that reach their target audience. Reinforcement Learning may be used to develop marketing tools that can learn and adapt to new data. This course may help you build a foundation in reinforcement learning and prepare you for a career as a Marketing Manager.

See salaries and explore the career path for Marketing Manager

Reading list

We've selected eight books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Reinforcement Learning.

Reinforcement Learning, second edition

Save

Comprehensive introduction to reinforcement learning, providing a solid foundation for understanding the fundamentals of the field. It covers theoretical concepts, algorithms, and applications in depth.

Reinforcement Learning, second edition: An...

Hardcover

$$$

Reinforcement Learning, second edition: An...

Kindle Edition

$$$

Reinforcement Learning, second edition

Save

Provides a comprehensive introduction to deep reinforcement learning, a rapidly growing field at the intersection of artificial intelligence and machine learning. It covers theoretical foundations, algorithms, and applications in depth.

Reinforcement Learning, second edition: An...

Hardcover

$$$

Reinforcement Learning, second edition: An...

Kindle Edition

$$$

Markov Decision Processes

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Provides a comprehensive introduction to Markov decision processes (MDPs), which are a fundamental framework for modeling sequential decision-making problems under uncertainty.

Markov Decision Processes: Discrete Stochastic...

Paperback

$$$$

Markov Decision Processes: Discrete Stochastic...

Kindle Edition

$$$$

Probabilistic Graphical Models

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Provides a comprehensive introduction to probabilistic graphical models, which are a powerful tool for representing and reasoning about complex probability distributions.

Probabilistic Graphical Models: Principles and...

Hardcover

$$$$

Probabilistic Graphical Models: Principles and...

Kindle Edition

$$$

Bayesian Reasoning and Machine Learning South Asian...

Save

Provides a comprehensive introduction to Bayesian reasoning and machine learning. It covers theoretical foundations, algorithms, and applications in depth.

Bayesian Reasoning and Machine Learning Paperback

Paperback

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Probabilistic Machine Learning

Save

Provides a comprehensive introduction to machine learning from a probabilistic perspective. It covers theoretical foundations, algorithms, and applications in depth.

Probabilistic Machine Learning: Advanced Topics...

Hardcover

$$$$

Getting Started with Google BERT

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Provides a practical introduction to reinforcement learning using Python. It covers theoretical foundations, algorithms, and applications in a hands-on manner.

Getting Started with Google BERT

Paperback

Check price

Getting Started with Google BERT

Kindle Edition

Check price

Deep Learning Techniques for Music Generation

Save

This video course provides a comprehensive introduction to reinforcement learning. It covers theoretical foundations, algorithms, and applications in a clear and engaging manner.

Deep Learning Techniques for Music Generation...

Kindle Edition

$$$$

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Similar courses

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Via

Udacity

Instructors

Charles Isbell

Michael Littman

Chris Pryby

Language

English

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Taught by experts like Michael Littman and Chris Pryby, who are well-known for their research and contributions to the field of reinforcement learning

Offers a comprehensive and structured approach to reinforcement learning concepts

Provides a strong foundation for understanding the basics of reinforcement learning, making it suitable for beginners in the field

Includes hands-on labs and resources, enabling learners to apply reinforcement learning concepts in practice

May be a good fit for individuals with a background in machine learning or related fields who are looking to enhance their understanding of reinforcement learning

Assumes some familiarity with basic machine learning concepts, which may be a barrier for those completely new to the field

Reinforcement Learning

Here's a deal for you

What's inside

Syllabus

Traffic lights

Save this course

Reviews summary

Deep theoretical dive into reinforcement learning

Activities

Career center

Reading list

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