Prediction and Control with Function Approximation

Go to Course: https://www.coursera.org/learn/prediction-control-function-approximation

Introduction

# Course Review: Prediction and Control with Function Approximation ## Overview The "Prediction and Control with Function Approximation" course is the third installment in the highly regarded Reinforcement Learning Specialization offered by the University of Alberta on Coursera. This course is crucial for anyone seeking to excel in reinforcement learning (RL), particularly in environments characterized by large, high-dimensional, and potentially infinite state spaces. It brilliantly ties together the concepts of function approximation and value function estimation, offering learners a chance to develop their skills in building agents capable of maximizing rewards through careful generalization and discrimination. ## Course Structure and Content ### Welcome to the Course! The course kicks off with an introductory module where you’ll get to know your instructors and fellow students. This engaging start sets a warm tone and encourages community interaction right from the beginning. It's a perfect opportunity to network with others passionate about reinforcement learning. ### On-policy Prediction with Approximation In the first week, you delve into estimating a value function for a given policy, which is particularly critical when the number of states exceeds the agent's memory capabilities. You will learn to specify a parametric form of the value function and establish an objective function. Learning about gradient descent in this context allows you to appreciate how agents can learn effectively through interaction with their environment. ### Constructing Features for Prediction The second week emphasizes the significance of feature construction in creating reliable value estimates. The two primary strategies discussed—fixed basis features and adaptive features through neural networks—are critical in ensuring your learning algorithm performs well. The hands-on graded assessment using a Neural Network in a task involving infinite state prediction is especially rewarding, reinforcing theoretical knowledge with practical application. ### Control with Approximation In the third week, the course builds upon concepts learned previously, extending classic temporal difference (TD) control methods to function approximation. Learners are guided on how to find the optimal policy in infinite-state Markov Decision Processes (MDPs) through methods like Q-learning and Sarsa. The introduction of average reward problems as a new formulation in RL broadens your understanding and prepares you for real-world applications. ### Policy Gradient During the final week, the course explores policy gradient methods—an advanced alternative to value-function methods. You’ll learn about the directly tuned parameters of the policy and their benefits, particularly in continuous state and action spaces. This week is particularly valuable for those looking to deepen their understanding of modern RL techniques, essential in many cutting-edge applications. ## Recommendations I highly recommend this course to anyone with a background in reinforcement learning who wishes to deepen their understanding of function approximation techniques. The blend of theoretical knowledge and practical assessments enhances the learning experience, making complex concepts digestible and applicable. ### Why Take This Course? 1. **Expert Instruction**: The course is led by knowledgeable instructors from the University of Alberta, renowned for their contributions to AI and RL. 2. **Hands-On Learning**: The graded assessments and hands-on projects ensure that learners can apply their knowledge in practical scenarios, reinforcing what they learn. 3. **Strong Foundations**: This course solidifies your grasp of RL concepts while expanding your toolbox with function approximation techniques essential for handling real-world problems. 4. **Community Engagement**: The inclusion of community interactions in the introductory module fosters a collaborative learning environment, which is invaluable for discussing ideas and concepts with peers. In conclusion, if you are keen on advancing your skills in reinforcement learning and want to develop robust agents for complex environments, "Prediction and Control with Function Approximation" on Coursera is a fantastic choice. Enroll today, and take the next step in your RL journey!

Syllabus

Welcome to the Course!

Welcome to the third course in the Reinforcement Learning Specialization: Prediction and Control with Function Approximation, brought to you by the University of Alberta, Onlea, and Coursera. In this pre-course module, you'll be introduced to your instructors, and get a flavour of what the course has in store for you. Make sure to introduce yourself to your classmates in the "Meet and Greet" section!

This week you will learn how to estimate a value function for a given policy, when the number of states is much larger than the memory available to the agent. You will learn how to specify a parametric form of the value function, how to specify an objective function, and how estimating gradient descent can be used to estimate values from interaction with the world.

The features used to construct the agent’s value estimates are perhaps the most crucial part of a successful learning system. In this module we discuss two basic strategies for constructing features: (1) fixed basis that form an exhaustive partition of the input, and (2) adapting the features while the agent interacts with the world via Neural Networks and Backpropagation. In this week’s graded assessment you will solve a simple but infinite state prediction task with a Neural Network and TD learning.

This week, you will see that the concepts and tools introduced in modules two and three allow straightforward extension of classic TD control methods to the function approximation setting. In particular, you will learn how to find the optimal policy in infinite-state MDPs by simply combining semi-gradient TD methods with generalized policy iteration, yielding classic control methods like Q-learning, and Sarsa. We conclude with a discussion of a new problem formulation for RL---average reward---which will undoubtedly be used in many applications of RL in the future.

Every algorithm you have learned about so far estimates a value function as an intermediate step towards the goal of finding an optimal policy. An alternative strategy is to directly learn the parameters of the policy. This week you will learn about these policy gradient methods, and their advantages over value-function based methods. You will also learn how policy gradient methods can be used to find the optimal policy in tasks with both continuous state and action spaces.