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Prediction and Control with Function Approximation

Master advanced reinforcement learning techniques for large-scale problems using function approximation and neural networks in continuous spaces.

Master advanced reinforcement learning techniques for large-scale problems using function approximation and neural networks in continuous spaces.

This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Reinforcement Learning Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.

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English

پښتو, বাংলা, اردو, 3 more

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Prediction and Control with Function Approximation

This course includes

21 Hours

Of Self-paced video lessons

Intermediate Level

Completion Certificate

awarded on course completion

Free course

What you'll learn

  • Implement TD learning with function approximation

  • Work with neural networks for value estimation

  • Understand policy gradient methods

  • Apply control algorithms in continuous spaces

  • Master actor-critic implementation

Skills you'll gain

Function Approximation
Reinforcement Learning
Machine Learning
Neural Networks
Policy Gradient Methods
TD Learning
Continuous State Spaces
Actor-Critic Methods
Deep Learning
AI Systems

This course includes:

3.5 Hours PreRecorded video

4 quizzes

Access on Mobile, Tablet, Desktop

FullTime access

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There are 5 modules in this course

This advanced course explores function approximation methods in reinforcement learning for handling large-scale problems. Students learn to implement TD learning with neural networks, policy gradient methods, and actor-critic algorithms. The curriculum covers both theoretical foundations and practical applications in continuous state and action spaces, preparing learners for real-world AI challenges.

Welcome to the Course!

Module 1 · 0 Hours to complete

On-policy Prediction with Approximation

Module 2 · 4 Hours to complete

Constructing Features for Prediction

Module 3 · 5 Hours to complete

Control with Approximation

Module 4 · 5 Hours to complete

Policy Gradient

Module 5 · 5 Hours to complete

Fee Structure

Instructors

Martha White
Martha White

4.7 rating

802 Reviews

99,327 Students

4 Courses

Innovator in Machine Learning and Reinforcement Learning

Martha White is an Assistant Professor in the Department of Computing Sciences at the University of Alberta, where she specializes in developing algorithms for agents that learn continuously from streams of data, focusing on representation learning and reinforcement learning. As a Principal Investigator at the Alberta Machine Intelligence Institute (AMII) and director of the Reinforcement Learning and Artificial Intelligence Lab (RLAI), she is at the forefront of research aimed at enhancing adaptive learning systems. Martha holds a Ph.D. in Computing Science from the University of Alberta and has published extensively on topics related to machine learning, including over 40 papers in top-tier journals and conferences. Her commitment to advancing knowledge in AI is complemented by her passion for mentoring emerging researchers and promoting diversity in computing. Outside of her academic pursuits, Martha enjoys soccer, outdoor activities, cooking, and reading science fiction, reflecting her diverse interests beyond the realm of technology.

Adam White
Adam White

4.7 rating

802 Reviews

99,327 Students

4 Courses

Leader in Reinforcement Learning and Artificial Intelligence

Adam White is an Assistant Professor in the Department of Computing Sciences at the University of Alberta and a Senior Research Scientist at DeepMind. His research centers on artificial intelligence, specifically on replicating or simulating human-level intelligence in both physical and simulated agents. Adam's work explores how intelligence can be modeled through reinforcement learning agents that interact with unknown environments, learning from scalar reward signals rather than explicit feedback. He has taught courses on Reinforcement Learning and Artificial Intelligence at both the University of Alberta and Indiana University, contributing to the education of future leaders in AI. As a Principal Investigator of the Reinforcement Learning and Artificial Intelligence Lab (RLAI) and a fellow at the Alberta Machine Intelligence Institute (AMII), Adam is dedicated to advancing the field through innovative research and practical applications. His contributions include developing new algorithms for reinforcement learning, creating the RL-Glue framework for reinforcement learning experiments, and demonstrating learning capabilities in mobile robots. Outside of academia, Adam enjoys playing Gaelic football and exploring the natural world, reflecting his diverse interests beyond his professional pursuits.

Prediction and Control with Function Approximation

This course includes

21 Hours

Of Self-paced video lessons

Intermediate Level

Completion Certificate

awarded on course completion

Free course

Testimonials

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Frequently asked questions

Below are some of the most commonly asked questions about this course. We aim to provide clear and concise answers to help you better understand the course content, structure, and any other relevant information. If you have any additional questions or if your question is not listed here, please don't hesitate to reach out to our support team for further assistance.