Enroll Course: https://www.coursera.org/learn/prediction-control-function-approximation
In the ever-evolving landscape of Artificial Intelligence, Reinforcement Learning (RL) stands out as a powerful paradigm for creating intelligent agents. The University of Alberta, in collaboration with Onlea and Coursera, offers a specialized course, “Prediction and Control with Function Approximation,” that delves deep into the practical application of RL in complex scenarios. This course is a gem for anyone looking to move beyond basic RL concepts and tackle problems with vast, high-dimensional, or even infinite state spaces.
The course begins by building upon foundational prediction methods like Monte Carlo and Temporal Difference (TD) learning. It masterfully explains how to extend these techniques to situations where the state space is too large to be enumerated. The key lies in function approximation, where you learn to represent value functions parametrically. This module is crucial for understanding how to balance generalization (learning from seen states) and discrimination (focusing on relevant states) to maximize rewards. You’ll explore how gradient descent can be employed to learn these value functions through interaction with the environment.
A significant portion of the course is dedicated to the art of constructing features for prediction. The instructors highlight that the quality of features is paramount for a successful learning system. They introduce two primary strategies: fixed basis functions that create an exhaustive partition of the input space, and adaptive features that evolve with the agent’s experience, notably through Neural Networks and Backpropagation. The hands-on experience gained here, especially in solving an infinite state prediction task using a Neural Network and TD learning, is invaluable.
The latter half of the course smoothly transitions to control problems. You’ll discover how the prediction tools learned earlier can be directly applied to control methods in large state spaces. Classic algorithms like Q-learning and Sarsa are revisited and extended to handle infinite state Markov Decision Processes (MDPs) by combining semi-gradient TD methods with generalized policy iteration. The introduction to the ‘average reward’ problem formulation is also a forward-thinking addition, preparing learners for future RL applications.
Finally, the course introduces Policy Gradient methods, offering an alternative to value-function-based approaches. These methods directly learn the parameters of the policy, often leading to more stable and efficient learning, especially in tasks with continuous state and action spaces. The comparison between value-function methods and policy gradient methods provides a comprehensive understanding of the RL toolkit.
Overall, “Prediction and Control with Function Approximation” is an exceptional course. It bridges the gap between theoretical RL concepts and practical, scalable solutions. The blend of clear explanations, practical examples, and hands-on assessments makes it highly recommendable for anyone serious about mastering reinforcement learning for complex, real-world problems. Whether you’re a student, researcher, or practitioner, this course will equip you with the advanced techniques needed to build sophisticated RL agents.
Enroll Course: https://www.coursera.org/learn/prediction-control-function-approximation