Implement a complete reinforcement learning solution, from problem formulation to empirical validation, in this hands-on capstone project.
Implement a complete reinforcement learning solution, from problem formulation to empirical validation, in this hands-on capstone project.
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
What you'll learn
Formalize real problems as MDPs
Select and implement appropriate RL algorithms
Design neural network function approximation
Conduct empirical studies of RL systems
Validate and assess system performance
Skills you'll gain
This course includes:
2.5 Hours PreRecorded video
2 quizzes
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FullTime access
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There are 6 modules in this course
This capstone project integrates key concepts from the Reinforcement Learning Specialization into a complete solution. Students work through all stages of RL system development, including problem formulation, algorithm selection, parameter tuning, and empirical validation. The course emphasizes practical implementation skills and scientific assessment of RL systems.
Welcome to the Final Capstone Course!
Module 1 · 0 Hours to complete
Milestone 1: Formalize Word Problem as MDP
Module 2 · 3 Hours to complete
Milestone 2: Choosing The Right Algorithm
Module 3 · 0 Hours to complete
Milestone 3: Identify Key Performance Parameters
Module 4 · 1 Hours to complete
Milestone 4: Implement Your Agent
Module 5 · 8 Hours to complete
Milestone 5: Submit Your Parameter Study!
Module 6 · 1 Hours to complete
Fee Structure
Instructors
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.
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.
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