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Like many other areas of machine learning research, reinforcement learning (RL) is evolving at breakneck speed. Just as they have done in other research areas, researchers are leveraging deep learning to achieve state-of-the-art results.
In particular,...
Read more
Like many other areas of machine learning research, reinforcement learning (RL) is evolving at breakneck speed. Just as they have done in other research areas, researchers are leveraging deep learning to achieve state-of-the-art results.
In particular,...
Read more
Like many other areas of machine learning research, reinforcement learning (RL) is evolving at breakneck speed. Just as they have done in other research areas, researchers are leveraging deep learning to achieve state-of-the-art results.
In particular, reinforcement learning has significantly outperformed prior ML techniques in game playing, reaching human-level and even world-best performance on Atari, beating the human Go champion, and is showing promising results in more difficult games like Starcraft II.
In this Google Cloud Lab, you will learn the basics of reinforcement learning by building a simple game, which has been modeled off of a sample provided by OpenAI Gym.
Note: you will have timed access to the online environment. You will need to complete the lab within the allotted time.
Register for this course and see more details by visiting:
OpenCourser.com/course/ztz2qn/reinforcement
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what to watch for , and
possible dealbreakers
Develops deep learning and reinforcement learning, which are core skills for game playing and other areas
Provides a practical hands-on approach through the lab component, allowing learners to apply the concepts immediately
Introduces the basics of reinforcement learning, making it accessible to those new to the field
Leverages OpenAI Gym sample, which is a trusted resource in the reinforcement learning community
Time-limited access to the online environment may restrict the learner's ability to complete the lab at their own pace
Assumes basic familiarity with machine learning concepts and Python programming
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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 Reinforcement Learning: Qwik Start with these
activities:
Review Reinforcement Learning Fundamentals
Show steps
Ensure a solid foundation by reviewing the fundamental concepts of reinforcement learning.
Browse courses on
Machine Learning Fundamentals
Show steps
-
Revisit textbooks or online resources on reinforcement learning
-
Review notes or lecture materials
-
Solve practice problems and exercises
-
Discuss key concepts with a mentor or peer
Read 'Reinforcement Learning: An Introduction'
Show steps
Gain a comprehensive understanding of reinforcement learning by reading a foundational book on the subject.
View
Reinforcement Learning, second edition: An...
on Amazon
Show steps
-
Obtain a copy of the book
-
Read the book thoroughly
-
Take notes and highlight key concepts
-
Discuss the book with a mentor or peer
Follow Tutorials on Reinforcement Learning Techniques
Show steps
Gain a deeper understanding of advanced reinforcement learning techniques by following guided tutorials.
Show steps
-
Identify a reinforcement learning technique to learn
-
Find a reputable tutorial on the technique
-
Follow the tutorial and implement the technique
-
Experiment with the technique and explore its applications
Two other activities
Expand to see all activities and additional details
Show all five activities
Practice Reinforcement Learning Algorithms
Show steps
Reinforce reinforcement learning concepts by practicing the implementation of different algorithms.
Show steps
-
Choose an algorithm to implement
-
Implement the algorithm in a programming language
-
Test the algorithm on various environments
-
Analyze the results and make improvements
Create a Reinforcement Learning Project
Show steps
Apply reinforcement learning concepts by creating a project that solves a real-world problem.
Browse courses on
Machine Learning Projects
Show steps
-
Define the problem you want to solve
-
Design the reinforcement learning architecture
-
Implement the project in a programming language
-
Evaluate the performance of the project
-
Document the project and share it with others
Review Reinforcement Learning Fundamentals
Show steps
Ensure a solid foundation by reviewing the fundamental concepts of reinforcement learning.
Browse courses on
Machine Learning Fundamentals
Show steps
Show steps
Show steps
- Revisit textbooks or online resources on reinforcement learning
- Review notes or lecture materials
- Solve practice problems and exercises
- Discuss key concepts with a mentor or peer
Read 'Reinforcement Learning: An Introduction'
Show steps
Gain a comprehensive understanding of reinforcement learning by reading a foundational book on the subject.
View
Reinforcement Learning, second edition: An...
on Amazon
Show steps
Show steps
Show steps
- Obtain a copy of the book
- Read the book thoroughly
- Take notes and highlight key concepts
- Discuss the book with a mentor or peer
Follow Tutorials on Reinforcement Learning Techniques
Show steps
Gain a deeper understanding of advanced reinforcement learning techniques by following guided tutorials.
Show steps
Show steps
Show steps
- Identify a reinforcement learning technique to learn
- Find a reputable tutorial on the technique
- Follow the tutorial and implement the technique
- Experiment with the technique and explore its applications
Two other activities
Expand to see all activities and additional details
Show all five activities
Practice Reinforcement Learning Algorithms
Show steps
Reinforce reinforcement learning concepts by practicing the implementation of different algorithms.
Show steps
Show steps
Show steps
- Choose an algorithm to implement
- Implement the algorithm in a programming language
- Test the algorithm on various environments
- Analyze the results and make improvements
Create a Reinforcement Learning Project
Show steps
Apply reinforcement learning concepts by creating a project that solves a real-world problem.
Browse courses on
Machine Learning Projects
Show steps
Show steps
Show steps
- Define the problem you want to solve
- Design the reinforcement learning architecture
- Implement the project in a programming language
- Evaluate the performance of the project
- Document the project and share it with others
Career center
Learners who complete Reinforcement Learning: Qwik Start will develop knowledge and skills
that may be useful to these careers:
Artificial Intelligence Researcher
Artificial Intelligence Researcher
Reinforcement learning is a subfield of artificial intelligence that focuses on training agents to make decisions in complex environments. As an artificial intelligence researcher, you could use reinforcement learning to develop self-driving cars, optimize supply chains, or design personalized recommendations systems.
Machine Learning Engineer
Machine Learning Engineer
Reinforcement learning is a subfield of machine learning that focuses on training agents to make decisions in complex environments. As a machine learning engineer, you could use reinforcement learning to develop self-driving cars, optimize supply chains, or design personalized recommendations systems.
Data Scientist
Data Scientist
Reinforcement learning is used to develop AI-powered systems that can learn from their experiences and improve their performance over time. As a data scientist, you could use reinforcement learning to develop predictive models, optimize business strategies, or identify fraud.
Data Engineer
Data Engineer
Reinforcement learning is used to build systems that can learn from their experiences and improve their performance over time. As a data engineer, you could use reinforcement learning to develop self-optimizing data pipelines, automatically tune model parameters, or identify anomalies in data sets.
Robotics Engineer
Robotics Engineer
Reinforcement learning is used to develop robots that can learn from their experiences and improve their performance over time. As a robotics engineer, you could use reinforcement learning to develop self-driving cars, industrial robots, or even spacecraft.
control Theory Engineer
control Theory Engineer
Reinforcement learning is used to develop AI-powered systems that can learn from their experiences and improve their performance over time. As a control theory engineer, you could use reinforcement learning to develop self-driving cars, industrial robots, or even spacecraft.
Quantitative Analyst
Quantitative Analyst
Reinforcement learning is used to develop AI-powered systems that can learn from their experiences and improve their performance over time. As a quantitative analyst, you could use reinforcement learning to develop trading strategies, risk models, or asset allocation models.
Game Developer
Game Developer
Reinforcement learning is used to develop AI-powered game characters that can learn from their experiences and improve their performance over time. As a game developer, you could use reinforcement learning to create more challenging and engaging games.
User Experience Researcher
User Experience Researcher
Reinforcement learning is used to develop AI-powered systems that can learn from their experiences and improve their performance over time. As a user experience researcher, you could use reinforcement learning to develop personalized user experiences, optimize website design, or even design self-driving cars.
Speech Recognition Engineer
Speech Recognition Engineer
Reinforcement learning is used to develop AI-powered systems that can learn from their experiences and improve their performance over time. As a speech recognition engineer, you could use reinforcement learning to develop speech recognition systems, voice control systems, or even self-driving cars.
Computer Vision Engineer
Computer Vision Engineer
Reinforcement learning is used to develop AI-powered systems that can learn from their experiences and improve their performance over time. As a computer vision engineer, you could use reinforcement learning to develop object detection systems, image segmentation systems, or even self-driving cars.
Operations Research Analyst
Operations Research Analyst
Reinforcement learning is used to develop AI-powered systems that can learn from their experiences and improve their performance over time. As an operations research analyst, you could use reinforcement learning to develop supply chain optimization models, scheduling algorithms, or inventory management systems.
Software Engineer
Software Engineer
Reinforcement learning is used to develop self-optimizing software systems that can learn from their experiences and improve their performance over time. As a software engineer, you could use reinforcement learning to develop self-healing systems, anomaly detection systems, or even self-driving cars.
Product Manager
Product Manager
Reinforcement learning is used to develop AI-powered systems that can learn from their experiences and improve their performance over time. As a product manager, you could use reinforcement learning to develop self-optimizing products, improve user engagement, or even develop new business models.
Natural Language Processing Engineer
Natural Language Processing Engineer
Reinforcement learning is used to develop AI-powered systems that can learn from their experiences and improve their performance over time. As a natural language processing engineer, you could use reinforcement learning to develop machine translation systems, chatbots, or even text summarization systems.
For more career information including salaries, visit:
OpenCourser.com/course/ztz2qn/reinforcement
Reading list
We've selected 11 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: Qwik Start.
Classic text on reinforcement learning and provides a comprehensive overview of the field. It valuable resource for anyone who wants to learn the basics of reinforcement learning and is especially useful for those who are new to the field.
Provides a practical introduction to reinforcement learning with Python and is especially useful for those who have some prior experience with Python. It covers a variety of reinforcement learning algorithms and provides hands-on examples of how to use them.
Provides a practical introduction to deep reinforcement learning and is especially useful for those who have some prior experience with reinforcement learning. It covers a variety of deep reinforcement learning algorithms and provides hands-on examples of how to use them.
Provides a practical introduction to reinforcement learning for robotics and is especially useful for those who have some prior experience with robotics. It covers a variety of reinforcement learning algorithms and provides hands-on examples of how to use them in robotics applications.
Save
Comprehensive overview of deep learning and provides a good foundation for understanding deep reinforcement learning. It covers a variety of deep learning topics, including deep reinforcement learning, and is especially useful for those who are new to the field.
Comprehensive overview of pattern recognition and machine learning and provides a good foundation for understanding reinforcement learning. It covers a variety of machine learning algorithms, including reinforcement learning, and is especially useful for those who are new to the field.
Comprehensive overview of machine learning and provides a good foundation for understanding reinforcement learning. It covers a variety of machine learning algorithms, including reinforcement learning, and is especially useful for those who are new to the field.
Provides a probabilistic perspective on machine learning and is especially useful for those who want to understand the mathematical foundations of the field. It covers a variety of machine learning algorithms, including reinforcement learning, and is especially useful for those who have some prior experience with probability theory.
Provides a Bayesian perspective on machine learning and is especially useful for those who want to understand the mathematical foundations of the field. It covers a variety of machine learning algorithms, including reinforcement learning, and is especially useful for those who have some prior experience with probability theory.
Provides a more theoretical treatment of reinforcement learning and optimal control and is especially useful for those who want to understand the mathematical foundations of the field. It covers a variety of reinforcement learning algorithms and provides proofs of their convergence.
Provides a very basic overview of machine learning and is not especially useful for those who want to learn about reinforcement learning. It covers a variety of machine learning topics, but only briefly mentions reinforcement learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/ztz2qn/reinforcement
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Time
1 hours
Via
Coursera
Institution
Google Cloud
Instructor
Google Cloud Training
Good to know
Develops deep learning and reinforcement learning, which are core skills for game playing and other areas
Provides a practical hands-on approach through the lab component, allowing learners to apply the concepts immediately
Introduces the basics of reinforcement learning, making it accessible to those new to the field
Leverages OpenAI Gym sample, which is a trusted resource in the reinforcement learning community
Time-limited access to the online environment may restrict the learner's ability to complete the lab at their own pace
Assumes basic familiarity with machine learning concepts and Python programming
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