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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.
Register for this course and see more details by visiting:
OpenCourser.com/course/k5uuo7/udacity
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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
Read more
Syllabus
Introduction to Reinforcement Learning
1. Smoov & Curly's Bogus Journey
2. Reinforcement Learning Basics
3. TD and Friends
Read more
4. Convergence
5. AAA
6. Messing with Rewards
7. Exploring Exploration
8. Generalization
9. Partially Observable MDPs
10. Options
11A. Game Theory
11B. Game Theory Reloaded
11C. Game Theory Revolutions
12. CCC
Outroduction to Reinforcement Learning
Homeworks - OBSOLETE
Good to know
Good to know
Know what's good ,
what to watch for , 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
Save this course
Save Reinforcement Learning to your list so you can find it easily later:
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Reviews summary
Reinforcement learning: advanced concepts
This course on Reinforcement Learning's advanced concepts has received mixed reactions from students. Many students have praised the professors for their teaching skills, saying the way they interact with each other really helps to clarify concepts. However, students who are new to Reinforcement Learning have mentioned that this course is not a good starting point. Students should also be familiar with Java as they will be programming extensively in this language.
Engaging and Interactive
"The way in which the instructors teach is awesome."
"The constant interaction between both professors really clarifies concepts and helps to avoid some bias about certain topics."
Java Familiarity Required
"Additionally, you will be programming extensively in Java during this course."
Can Be Slow
"The pace was extremely slow and there were too much nonsense going on between the two speakers."
"I am not assessing the whole course cause I couldn't make it through the first week because I didn't like it very much. The pace is very slow and the course doesn't seem to be very thorough."
Not entry-level
"Not as a first-time Reinforcement Learning course."
"This course is a more advanced and fast-paced course in RL compared to standard RL literature."
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
Show steps
Establish connections with individuals working in reinforcement learning to gain insights and guidance.
Browse courses on
Mentorship
Show steps
-
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
Show steps
Gain a foundational understanding of reinforcement learning principles through interactive tutorials on Coursera.
Browse courses on
Reinforcement Learning
Show steps
-
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
Show steps
Reinforce concepts related to partially ordered sets and functions, which are fundamental for understanding reinforcement learning.
Browse courses on
Functions
Show steps
-
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
Expand to see all activities and additional details
Show all seven activities
Solve dynamic programming problems
Show steps
Strengthen problem-solving skills in dynamic programming, a technique closely related to reinforcement learning.
Browse courses on
Dynamic programming
Show steps
-
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
Show steps
Apply reinforcement learning concepts by creating a simple game that demonstrates the principles of learning and reward-based behavior.
Browse courses on
Game Development
Show steps
-
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
Show steps
Engage in real-world reinforcement learning challenges by participating in Kaggle competitions, testing your skills and learning from others.
Browse courses on
Kaggle Competitions
Show steps
-
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
Show steps
Apply your reinforcement learning knowledge to a practical business problem, showcasing your ability to bridge theory and practice.
Browse courses on
Business Applications
Show steps
-
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.
Connect with experienced practitioners in the field
Show steps
Establish connections with individuals working in reinforcement learning to gain insights and guidance.
Browse courses on
Mentorship
Show steps
Show steps
Show steps
- 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
Show steps
Gain a foundational understanding of reinforcement learning principles through interactive tutorials on Coursera.
Browse courses on
Reinforcement Learning
Show steps
Show steps
Show steps
- 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
Show steps
Reinforce concepts related to partially ordered sets and functions, which are fundamental for understanding reinforcement learning.
Browse courses on
Functions
Show steps
Show steps
Show steps
- 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
Expand to see all activities and additional details
Show all seven activities
Solve dynamic programming problems
Show steps
Strengthen problem-solving skills in dynamic programming, a technique closely related to reinforcement learning.
Browse courses on
Dynamic programming
Show steps
Show steps
Show steps
- 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
Show steps
Apply reinforcement learning concepts by creating a simple game that demonstrates the principles of learning and reward-based behavior.
Browse courses on
Game Development
Show steps
Show steps
Show steps
- 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
Show steps
Engage in real-world reinforcement learning challenges by participating in Kaggle competitions, testing your skills and learning from others.
Browse courses on
Kaggle Competitions
Show steps
Show steps
Show steps
- 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
Show steps
Apply your reinforcement learning knowledge to a practical business problem, showcasing your ability to bridge theory and practice.
Browse courses on
Business Applications
Show steps
Show steps
Show steps
- 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:
Deep 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.
Machine Learning Engineer
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.
Artificial Intelligence 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.
Data Scientist
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.
Deep 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.
Data Science 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.
Machine Learning Researcher
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.
Artificial Intelligence Engineer
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.
Financial 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.
Marketing 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.
Operations Research 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.
Software Engineer
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.
Data Analyst
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.
Management Consultant
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.
Product Manager
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.
For more career information including salaries, visit:
OpenCourser.com/course/k5uuo7/udacity
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.
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.
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.
Provides a comprehensive introduction to Markov decision processes (MDPs), which are a fundamental framework for modeling sequential decision-making problems under uncertainty.
Provides a comprehensive introduction to probabilistic graphical models, which are a powerful tool for representing and reasoning about complex probability distributions.
Provides a comprehensive introduction to Bayesian reasoning and machine learning. It covers theoretical foundations, algorithms, and applications in depth.
Provides a comprehensive introduction to machine learning from a probabilistic perspective. It covers theoretical foundations, algorithms, and applications in depth.
Provides a practical introduction to reinforcement learning using Python. It covers theoretical foundations, algorithms, and applications in a hands-on manner.
This video course provides a comprehensive introduction to reinforcement learning. It covers theoretical foundations, algorithms, and applications in a clear and engaging manner.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/k5uuo7/udacity
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Via
Udacity
Instructors
Charles Isbell
Michael Littman
Chris Pryby
Language
English
Good to know
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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