Enroll Course: https://www.coursera.org/learn/prediction-control-function-approximation
The course ‘Prediction and Control with Function Approximation’ offered by the University of Alberta on Coursera is an excellent resource for anyone interested in advanced reinforcement learning techniques. Designed for learners who have some background in RL, this course dives deep into handling large, high-dimensional, and potentially infinite state spaces, making it highly relevant for real-world applications.
Throughout the syllabus, the course covers key topics such as on-policy prediction with approximation, feature construction for prediction, control with approximation, and policy gradient methods. What sets this course apart is its practical approach—students learn to implement these techniques through hands-on assignments, including neural network-based prediction tasks and policy optimization exercises.
The instructor-led modules break down complex concepts like function approximation, gradient descent estimation, and policy optimization in an accessible manner, with clear examples and real-world relevance. The inclusion of cutting-edge topics like average reward formulation and continuous spaces expands learners’ understanding of modern RL challenges.
I highly recommend this course for intermediate to advanced learners aiming to hone their skills in reinforcement learning, especially those interested in deploying RL algorithms in complex environments. Whether you’re a data scientist, AI researcher, or software engineer, this course provides valuable insights and practical techniques to advance your RL journey.
Enroll Course: https://www.coursera.org/learn/prediction-control-function-approximation