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Mat Leonard,
Miguel Morales,
Chhavi Yadav,
Dana Sheahan,
Cezanne Camacho,
Alexis Cook,
Arpan Chakraborty,
Luis Serrano,
and
Juan Delgado
Register for this course and see more details by visiting:
OpenCourser.com/course/jq5hck/introduction
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What's inside
Syllabus
Welcome to the Deep Reinforcement Learning Nanodegree program!
You are starting a challenging but rewarding journey! Take 5 minutes to read how to get help with projects and content.
Read more
Syllabus
Welcome to the Deep Reinforcement Learning Nanodegree program!
You are starting a challenging but rewarding journey! Take 5 minutes to read how to get help with projects and content.
Read more
What to do if you have questions about your account or general questions about the program.
Obtain helpful resources to accelerate your learning in this first part of the Nanodegree program.
Reinforcement learning is a type of machine learning where the machine or software agent learns how to maximize its performance at a task.
Learn how to mathematically formulate tasks as Markov Decision Processes.
In reinforcement learning, agents learn to prioritize different decisions based on the rewards and punishments associated with different outcomes.
Write your own implementation of Monte Carlo control to teach an agent to play Blackjack!
Learn about how to apply temporal-difference methods such as SARSA, Q-Learning, and Expected SARSA to solve both episodic and continuing tasks.
With reinforcement learning now in your toolbox, you're ready to explore a mini project using OpenAI Gym!
Learn how to adapt traditional algorithms to work with continuous spaces.
In the next parts of the Nanodegree program, you'll learn all about how to use neural networks as powerful function approximators in reinforcement learning.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Teaches reinforcement learning, which is a subfield of machine learning growing in popularity in industry
Taught by a team of experienced instructors with expertise in reinforcement learning
Includes interactive materials and hands-on labs, providing practical experience
Develops foundational knowledge in Markov Decision Processes, temporal-difference methods, and function approximation
Prepares learners to apply reinforcement learning to real-world problems using OpenAI Gym
Requires prior programming experience and some understanding of machine learning concepts
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Save Introduction to Deep Reinforcement Learning to your list so you can find it easily later:
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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 Introduction to Deep Reinforcement Learning with these
activities:
Review core statistics and linear algebra fundamentals
Show steps
Reinforce your prior knowledge and prepare for the course's reliance on these concepts.
Show steps
-
Review concepts of matrix and vector operations, linear independence, and eigenvalues.
-
Practice solving systems of linear equations and finding determinants.
-
Refresh your understanding of probability distributions, statistical inference, and hypothesis testing.
Solve hands-on coding exercises related to reinforcement learning concepts
Show steps
Strengthen your grasp of reinforcement learning algorithms through practical coding exercises.
Show steps
-
Find and attempt coding exercises that focus on implementing reinforcement learning techniques.
-
Debug and optimize your solutions to improve their efficiency.
-
Compare your solutions with others and learn from different approaches.
Create a comprehensive study guide that summarizes key concepts, algorithms, and equations
Show steps
Consolidate your learning and prepare for assessments by organizing and reviewing course materials.
Show steps
-
Collect and organize notes, assignments, quizzes, and exams.
-
Summarize key concepts, algorithms, and equations into a cohesive guide.
-
Review the study guide regularly to reinforce understanding.
Show all three activities
Review core statistics and linear algebra fundamentals
Show steps
Reinforce your prior knowledge and prepare for the course's reliance on these concepts.
Show steps
Show steps
Show steps
- Review concepts of matrix and vector operations, linear independence, and eigenvalues.
- Practice solving systems of linear equations and finding determinants.
- Refresh your understanding of probability distributions, statistical inference, and hypothesis testing.
Solve hands-on coding exercises related to reinforcement learning concepts
Show steps
Strengthen your grasp of reinforcement learning algorithms through practical coding exercises.
Show steps
Show steps
Show steps
- Find and attempt coding exercises that focus on implementing reinforcement learning techniques.
- Debug and optimize your solutions to improve their efficiency.
- Compare your solutions with others and learn from different approaches.
Create a comprehensive study guide that summarizes key concepts, algorithms, and equations
Show steps
Consolidate your learning and prepare for assessments by organizing and reviewing course materials.
Show steps
Show steps
Show steps
- Collect and organize notes, assignments, quizzes, and exams.
- Summarize key concepts, algorithms, and equations into a cohesive guide.
- Review the study guide regularly to reinforce understanding.
Show all three activities
Career center
Learners who complete Introduction to Deep Reinforcement Learning will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and deploy machine learning models. Reinforcement learning is a powerful type of machine learning that can be used to solve a wide variety of problems. This course will teach you the fundamentals of reinforcement learning, which will give you a strong foundation for a career as a Machine Learning Engineer.
Data Scientist
Data Scientist
Data Scientists use machine learning, statistics, and programming to extract insights from data. Reinforcement learning is a type of machine learning that is well-suited to solving problems where the environment is dynamic and uncertain. This course will teach you the fundamentals of reinforcement learning, which will give you a strong foundation for a career as a Data Scientist.
Research Scientist
Research Scientist
Research Scientists conduct research in a variety of fields, including machine learning, artificial intelligence, and robotics. Reinforcement learning is a rapidly growing field of research, and this course will give you a strong foundation in the fundamentals of reinforcement learning. This will make you a more competitive candidate for research positions in this field.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematics, statistics, and programming to solve problems in finance. Reinforcement learning is a type of machine learning that can be used to solve a variety of problems in finance, including problems in portfolio optimization and risk management. This course will teach you the fundamentals of reinforcement learning, which will give you a strong foundation for a career as a Quantitative Analyst.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. Reinforcement learning is a type of machine learning that can be used to solve a wide variety of problems, including problems in software engineering. This course will teach you the fundamentals of reinforcement learning, which will give you a strong foundation for a career as a Software Engineer.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain data systems. Reinforcement learning is a type of machine learning that can be used to solve a variety of problems in data engineering, including problems in data cleaning and data integration. This course will teach you the fundamentals of reinforcement learning, which will give you a strong foundation for a career as a Data Engineer.
Business Analyst
Business Analyst
Business Analysts use data and analytics to solve business problems. Reinforcement learning is a type of machine learning that can be used to solve a variety of problems in business, including problems in customer segmentation and marketing optimization. This course will teach you the fundamentals of reinforcement learning, which will give you a strong foundation for a career as a Business Analyst.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematics, statistics, and programming to solve problems in operations management. Reinforcement learning is a type of machine learning that can be used to solve a variety of problems in operations management, including problems in supply chain management and inventory optimization. This course will teach you the fundamentals of reinforcement learning, which will give you a strong foundation for a career as an Operations Research Analyst.
Product Manager
Product Manager
Product Managers are responsible for the development and management of products. Reinforcement learning is a type of machine learning that can be used to solve a variety of problems in product development, including problems in product optimization and user experience. This course will teach you the fundamentals of reinforcement learning, which will give you a strong foundation for a career as a Product Manager.
Risk Manager
Risk Manager
Risk Managers identify, assess, and manage risks. Reinforcement learning is a type of machine learning that can be used to solve a variety of problems in risk management, including problems in portfolio optimization and risk assessment. This course will teach you the fundamentals of reinforcement learning, which will give you a strong foundation for a career as a Risk Manager.
Technical Writer
Technical Writer
Technical Writers create documentation for software and other technical products. Reinforcement learning is a type of machine learning that can be used to solve a variety of problems in technical writing, including problems in generating documentation and translating documentation into different languages. This course will teach you the fundamentals of reinforcement learning, which will give you a strong foundation for a career as a Technical Writer.
Consultant
Consultant
Consultants provide advice and guidance to organizations on a variety of topics. Reinforcement learning is a type of machine learning that can be used to solve a variety of problems in consulting, including problems in business strategy and organizational design. This course will teach you the fundamentals of reinforcement learning, which will give you a strong foundation for a career as a Consultant.
User Experience Designer
User Experience Designer
User Experience Designers design and evaluate user interfaces. Reinforcement learning is a type of machine learning that can be used to solve a variety of problems in user experience design, including problems in optimizing user interfaces and personalizing user experiences. This course will teach you the fundamentals of reinforcement learning, which will give you a strong foundation for a career as a User Experience Designer.
Entrepreneur
Entrepreneur
Entrepreneurs start and run their own businesses. Reinforcement learning is a type of machine learning that can be used to solve a variety of problems in entrepreneurship, including problems in product development and marketing optimization. This course will teach you the fundamentals of reinforcement learning, which will give you a strong foundation for a career as an Entrepreneur.
Teacher
Teacher
Teachers educate students at all levels. Reinforcement learning is a type of machine learning that can be used to solve a variety of problems in education, including problems in personalized learning and adaptive assessment. This course will teach you the fundamentals of reinforcement learning, which will give you a strong foundation for a career as a Teacher.
For more career information including salaries, visit:
OpenCourser.com/course/jq5hck/introduction
Reading list
We've selected ten 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
Introduction to Deep Reinforcement Learning.
Is commonly used as a textbook at academic institutions and industry professionals. It provides comprehensive coverage of foundational concepts in reinforcement learning, discussing various algorithms and techniques.
Provides a comprehensive treatment of reinforcement learning and optimal control theory. It offers a rigorous and mathematical approach to the subject, making it a valuable resource for researchers and advanced students.
Provides a comprehensive overview of reinforcement learning algorithms, covering both classical and modern techniques. It offers a rigorous treatment of the subject, making it a valuable resource for students and researchers in the field.
Provides a deep dive into Markov decision processes (MDPs), which are essential for understanding the mathematical foundations of reinforcement learning. It covers advanced topics such as policy evaluation and optimization, making it a valuable reference for those seeking a more rigorous treatment of MDPs.
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While not specifically focused on reinforcement learning, this book provides a comprehensive overview of deep learning techniques that are commonly used in reinforcement learning algorithms. It offers insights into neural networks, convolutional neural networks, and recurrent neural networks, which are essential for understanding the state-of-the-art in deep reinforcement learning.
Provides a probabilistic perspective on machine learning, which is essential for understanding the theoretical foundations of reinforcement learning. It covers topics such as Bayesian inference, graphical models, and variational inference, providing a solid background for further study in reinforcement learning.
Provides a strong foundation in probability and statistics, which are essential for understanding the mathematical underpinnings of reinforcement learning. It covers topics such as random variables, probability distributions, and statistical inference, providing a solid background for further study in reinforcement learning.
Provides a comprehensive overview of machine learning, including reinforcement learning. It offers a gentle introduction to the field, making it suitable for beginners and those seeking a broad understanding of machine learning concepts.
Provides a foundation in causal inference, which is crucial for understanding the impact of actions in reinforcement learning. It covers topics such as counterfactuals, graphical models, and causal diagrams, enabling learners to reason about cause-and-effect relationships in decision-making.
Provides a comprehensive treatment of convex optimization, which powerful tool for solving optimization problems that arise in reinforcement learning. It covers topics such as linear programming, quadratic programming, and semidefinite programming, providing a solid foundation for understanding advanced reinforcement learning algorithms.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/jq5hck/introduction
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Time
2032 hours
Level
Advanced
Via
Udacity
Instructors
Mat Leonard
Miguel Morales
Chhavi Yadav
Dana Sheahan
Cezanne Camacho
Alexis Cook
Arpan Chakraborty
Luis Serrano
Juan Delgado
Language
English
Good to know
Teaches reinforcement learning, which is a subfield of machine learning growing in popularity in industry
Taught by a team of experienced instructors with expertise in reinforcement learning
Includes interactive materials and hands-on labs, providing practical experience
Develops foundational knowledge in Markov Decision Processes, temporal-difference methods, and function approximation
Prepares learners to apply reinforcement learning to real-world problems using OpenAI Gym
Requires prior programming experience and some understanding of machine learning concepts
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