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Charles Ivan Niswander II
By the end of this project you will learn how to train a reinforcement learning agent to play Atari video games autonomously using Deep Q-Learning with Tensorflow and OpenAI's Gym API. This project will familiarize you with the Gym interface and the process...
Read more
By the end of this project you will learn how to train a reinforcement learning agent to play Atari video games autonomously using Deep Q-Learning with Tensorflow and OpenAI's Gym API. This project will familiarize you with the Gym interface and the process...
Read more
By the end of this project you will learn how to train a reinforcement learning agent to play Atari video games autonomously using Deep Q-Learning with Tensorflow and OpenAI's Gym API. This project will familiarize you with the Gym interface and the process of training a Tensorflow-based neural network using Deep Q-Learning techniques. The methods you will learn in the course of this project will enable you to build reinforcement learning agents for any potential purpose and provide valuable experience in your Machine Learning and Artificial Intelligence development journey.
Python experience is heavily recommended.
Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
Register for this course and see more details by visiting:
OpenCourser.com/course/vhk95g/tensorflow
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Deeply immerses learners in the practices of reinforcement learning, making it a valuable resource for those interested in AI and Machine Learning
Notably focuses on Atari video games, which are fundamental in testing reinforcement learning agents, thus providing hands-on experience in a concrete and popular application of reinforcement learning
Leverages Deep Q-Learning with Tensorflow, preparing learners with the latest and highly applicable tools and techniques in the field
Integrates OpenAI's Gym API for training reinforcement learning agents, aligning with industry-standard tools and enabling transferability of the learned methods to other environments
Includes hands-on projects and labs, fostering practical implementation and experimentation, which is essential for developing robust understanding and skills in reinforcement learning
Course has a particular affinity with learners and professionals interested in building reinforcement learning agents for various purposes, making it highly relevant to those pursuing careers in AI, Machine Learning, and related fields
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Save Tensorflow Neural Networks using Deep Q-Learning Techniques to your list so you can find it easily later:
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Reviews summary
Challenging deep q-learning with tensorflow
This course dives into reinforcement learning and provides a base for building reinforcement learning agents. Students with Python experience familiar with deep learning may find this course more manageable. The learning materials may be out of reach for beginners. Some students experienced issues with the availability of course videos and code.
The course provides a good starting point for reinforcement learning.
"... should provide a good starting point for further exploration ..."
Some students have had issues with access to videos and code.
"... And after some time all the videos, codes & virtual machine disappear ..."
The code is not always easy to follow.
"... The script in the video is difficult to follow ..."
This course may be difficult for beginners.
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 Tensorflow Neural Networks using Deep Q-Learning Techniques with these
activities:
Review Python programming basics
Show steps
Review Python programming basics to ensure you have a strong foundation for the course.
Browse courses on
Python
Show steps
-
Review the basics of Python syntax and data structures.
-
Practice writing simple Python programs.
Read Sutton and Barto's 'Reinforcement Learning: An Introduction'
Show steps
Read Sutton and Barto's 'Reinforcement Learning: An Introduction' to gain a deeper understanding of the theoretical foundations of reinforcement learning.
View
Reinforcement Learning, second edition: An...
on Amazon
Show steps
-
Read Chapter 2: 'Basic Concepts of Reinforcement Learning'
-
Read Chapter 4: 'Value Functions'
-
Read Chapter 6: 'Policy Gradient Methods'
Follow tutorials on OpenAI Gym
Show steps
Follow tutorials on OpenAI Gym to gain familiarity with the library used in the course.
Browse courses on
OpenAI Gym
Show steps
-
Complete the 'Getting Started with OpenAI Gym' tutorial.
-
Complete the 'CartPole-v0' tutorial.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Practice Deep Q-Learning algorithms
Show steps
Practice Deep Q-Learning algorithms to reinforce your understanding of the concepts and techniques covered in the course.
Browse courses on
Deep Q-Learning
Show steps
-
Implement a basic Deep Q-Learning algorithm in Python.
-
Train the algorithm on a simple Atari game environment.
-
Evaluate the performance of the trained algorithm.
Mentor other students in the course
Show steps
Mentor other students in the course to reinforce your understanding of the concepts and techniques covered in the course.
Show steps
-
Identify a student who needs help.
-
Offer your help and provide guidance.
-
Answer questions and provide feedback.
Volunteer at a local AI or machine learning organization
Show steps
Volunteer at a local AI or machine learning organization to gain practical experience and connect with others in the field.
Browse courses on
AI
Show steps
-
Find a local AI or machine learning organization that offers volunteer opportunities.
-
Contact the organization and express your interest in volunteering.
-
Attend volunteer training to learn about the organization and its mission.
Build a reinforcement learning agent for a custom Atari game
Show steps
Build a reinforcement learning agent for a custom Atari game to apply your understanding of the concepts and techniques covered in the course.
Browse courses on
Reinforcement Learning
Show steps
-
Choose an Atari game to work with.
-
Design and implement a reinforcement learning algorithm for the game.
-
Train and evaluate the performance of the algorithm.
Write a blog post about your experiences applying reinforcement learning to Atari games
Show steps
Write a blog post about your experiences applying reinforcement learning to Atari games to share your knowledge and insights with others.
Browse courses on
Reinforcement Learning
Show steps
-
Choose a topic to write about, such as the challenges you faced, the techniques you used, or the lessons you learned.
-
Write a draft of your blog post.
-
Edit and revise your blog post.
Review Python programming basics
Show steps
Review Python programming basics to ensure you have a strong foundation for the course.
Browse courses on
Python
Show steps
Show steps
Show steps
- Review the basics of Python syntax and data structures.
- Practice writing simple Python programs.
Read Sutton and Barto's 'Reinforcement Learning: An Introduction'
Show steps
Read Sutton and Barto's 'Reinforcement Learning: An Introduction' to gain a deeper understanding of the theoretical foundations of reinforcement learning.
View
Reinforcement Learning, second edition: An...
on Amazon
Show steps
Show steps
Show steps
- Read Chapter 2: 'Basic Concepts of Reinforcement Learning'
- Read Chapter 4: 'Value Functions'
- Read Chapter 6: 'Policy Gradient Methods'
Follow tutorials on OpenAI Gym
Show steps
Follow tutorials on OpenAI Gym to gain familiarity with the library used in the course.
Browse courses on
OpenAI Gym
Show steps
Show steps
Show steps
- Complete the 'Getting Started with OpenAI Gym' tutorial.
- Complete the 'CartPole-v0' tutorial.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Practice Deep Q-Learning algorithms
Show steps
Practice Deep Q-Learning algorithms to reinforce your understanding of the concepts and techniques covered in the course.
Browse courses on
Deep Q-Learning
Show steps
Show steps
Show steps
- Implement a basic Deep Q-Learning algorithm in Python.
- Train the algorithm on a simple Atari game environment.
- Evaluate the performance of the trained algorithm.
Mentor other students in the course
Show steps
Mentor other students in the course to reinforce your understanding of the concepts and techniques covered in the course.
Show steps
Show steps
Show steps
- Identify a student who needs help.
- Offer your help and provide guidance.
- Answer questions and provide feedback.
Volunteer at a local AI or machine learning organization
Show steps
Volunteer at a local AI or machine learning organization to gain practical experience and connect with others in the field.
Browse courses on
AI
Show steps
Show steps
Show steps
- Find a local AI or machine learning organization that offers volunteer opportunities.
- Contact the organization and express your interest in volunteering.
- Attend volunteer training to learn about the organization and its mission.
Build a reinforcement learning agent for a custom Atari game
Show steps
Build a reinforcement learning agent for a custom Atari game to apply your understanding of the concepts and techniques covered in the course.
Browse courses on
Reinforcement Learning
Show steps
Show steps
Show steps
- Choose an Atari game to work with.
- Design and implement a reinforcement learning algorithm for the game.
- Train and evaluate the performance of the algorithm.
Write a blog post about your experiences applying reinforcement learning to Atari games
Show steps
Write a blog post about your experiences applying reinforcement learning to Atari games to share your knowledge and insights with others.
Browse courses on
Reinforcement Learning
Show steps
Show steps
Show steps
- Choose a topic to write about, such as the challenges you faced, the techniques you used, or the lessons you learned.
- Write a draft of your blog post.
- Edit and revise your blog post.
Career center
Learners who complete Tensorflow Neural Networks using Deep Q-Learning Techniques will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers create artificial intelligence (AI)-based systems that can learn, make decisions, and complete tasks without being explicitly programmed. Using Tensorflow, engineers are able to leverage a framework that enables the creation of flexible, highly efficient neural networks for complex data problems. Deep Q-Learning, an advanced technique used to train AI agents, can be executed using open-source Tensorflow libraries. This course correlates with job duties by providing expertise in the use of these Tensorflow tools to train reinforcement learning agents to play Atari video games autonomously. The techniques learned in this course can be applied to build reinforcement learning agents for other purposes, which would be highly relevant in this role.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
Artificial Intelligence Engineers design, develop, and maintain AI systems. This course would be highly useful for Artificial Intelligence Engineers because it can help them build a foundation in Tensorflow Deep Q-Learning techniques. Artificial Intelligence Engineers can use these techniques to train AI agents to solve complex problems in various industries.
Artificial Intelligence Researcher
Artificial Intelligence Researcher
Artificial Intelligence Researchers develop new AI algorithms and techniques. This course may be useful for Artificial Intelligence Researchers who want to specialize in Deep Q-Learning.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. The skills taught in this course may be useful for Software Engineers who want to specialize in AI system development.
Robotics Engineer
Robotics Engineer
Robotics Engineers design, develop, and maintain robots. The skills taught in this course may be useful for Robotics Engineers who want to develop AI-powered robots.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data. The skills taught in this course may be useful for Quantitative Analysts who want to use AI techniques to develop trading strategies.
Product Manager
Product Manager
Product Managers develop and manage products. The skills taught in this course may be useful for Product Managers who want to develop AI-powered products.
Data Scientist
Data Scientist
Data Scientists collect, analyze, interpret, and visualize data to drive data-informed decision making. The skills taught in this course may be useful for Data Scientists who want to advance their capabilities in AI system building.
Business Analyst
Business Analyst
Business Analysts analyze business processes and identify opportunities for improvement. The skills taught in this course may be useful for Business Analysts who want to use AI techniques to improve business processes.
Data Analyst
Data Analyst
Data Analysts collect, clean, and analyze data to help organizations make data-driven decisions. The skills taught in this course may be useful for Data Analysts who want to advance their capabilities in AI system building.
Management Consultant
Management Consultant
Management Consultants help organizations improve their performance. The skills taught in this course may be useful for Management Consultants who want to use AI techniques to help their clients improve their performance.
IT Consultant
IT Consultant
IT Consultants help organizations with their IT systems. The skills taught in this course may be useful for IT Consultants who want to help their clients develop AI-powered IT systems.
Technical Writer
Technical Writer
Technical Writers create user manuals, white papers, and other technical documents. The skills taught in this course may be useful for Technical Writers who want to write about AI systems.
Teacher
Teacher
Teachers educate students in a variety of subjects. The skills taught in this course may be useful for Teachers who want to teach AI to their students.
Student
Student
Students are enrolled in an educational program to learn new skills and knowledge. This course may be useful for Students who are interested in learning about AI.
For more career information including salaries, visit:
OpenCourser.com/course/vhk95g/tensorflow
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
Tensorflow Neural Networks using Deep Q-Learning Techniques.
Provides a comprehensive introduction to the field of reinforcement learning, covering the fundamental concepts and algorithms. It valuable resource for anyone interested in learning about this important area of machine learning.
Save
Provides a comprehensive introduction to deep learning, a subfield of machine learning that has achieved remarkable results in recent years. It valuable resource for anyone interested in learning about the field of deep learning.
Provides a comprehensive introduction to probabilistic robotics, a field that combines robotics and probability theory. It valuable resource for anyone interested in learning about the field of probabilistic robotics.
Save
Provides a comprehensive introduction to computer vision, a field that deals with the extraction of information from images. It valuable resource for anyone interested in learning about the field of computer vision.
Provides a comprehensive introduction to natural language processing, a field that deals with the processing of human language. It valuable resource for anyone interested in learning about the field of natural language processing.
Provides a comprehensive introduction to speech and language processing, a field that deals with the processing of spoken and written language. It valuable resource for anyone interested in learning about the field of speech and language processing.
Provides a comprehensive introduction to information theory, inference, and learning algorithms. It valuable resource for anyone interested in learning about the field of information theory, inference, and learning algorithms.
Provides a comprehensive introduction to machine learning from a probabilistic perspective. It valuable resource for anyone interested in learning about the field of machine learning from a probabilistic perspective.
Provides a comprehensive introduction to Bayesian reasoning and machine learning. It valuable resource for anyone interested in learning about the field of Bayesian reasoning and machine learning.
Provides a comprehensive introduction to pattern recognition and machine learning. It valuable resource for anyone interested in learning about the field of pattern recognition and machine learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/vhk95g/tensorflow
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Time
2 hours
Via
Coursera
Institution
Coursera Project Network
Instructor
Charles Ivan Niswander II
Good to know
Deeply immerses learners in the practices of reinforcement learning, making it a valuable resource for those interested in AI and Machine Learning
Notably focuses on Atari video games, which are fundamental in testing reinforcement learning agents, thus providing hands-on experience in a concrete and popular application of reinforcement learning
Leverages Deep Q-Learning with Tensorflow, preparing learners with the latest and highly applicable tools and techniques in the field
Integrates OpenAI's Gym API for training reinforcement learning agents, aligning with industry-standard tools and enabling transferability of the learned methods to other environments
Includes hands-on projects and labs, fostering practical implementation and experimentation, which is essential for developing robust understanding and skills in reinforcement learning
Course has a particular affinity with learners and professionals interested in building reinforcement learning agents for various purposes, making it highly relevant to those pursuing careers in AI, Machine Learning, and related fields
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