Fundamentals of Reinforcement Learning

Go to Course: https://www.coursera.org/learn/fundamentals-of-reinforcement-learning

Introduction

**Course Review: Fundamentals of Reinforcement Learning on Coursera** If you’re interested in the burgeoning field of artificial intelligence and machine learning, particularly in how automated decision-making processes work in real-world applications, the "Fundamentals of Reinforcement Learning" course on Coursera is an exceptional starting point. Offered by the University of Alberta in collaboration with Onlea, this course not only lays the groundwork for understanding reinforcement learning (RL) but also equips you with practical skills to implement these concepts. **Overview of the Course** Reinforcement Learning is a critical subfield of machine learning that deals with how agents interact with their environment through trial and error, learning from consequences to make better decisions over time. The course is designed to guide you through the complexities of RL, starting from the fundamental principles to more advanced techniques. It is especially relevant as businesses increasingly seek to implement intelligent decision-making systems in various domains, from robotics to finance. **Syllabus Breakdown** The course is structured into several key modules, each carefully crafted to build your understanding step-by-step: 1. **Welcome to the Course!** - The introduction sets the tone for the entire specialization. You’ll meet your instructors and gain insights into how to navigate through the course successfully. 2. **An Introduction to Sequential Decision-Making** - This module delves into the exploration-exploitation trade-off, a critical concept in reinforcement learning. You’ll implement an epsilon-greedy agent, which is foundational for understanding how agents can make decisions in uncertain environments. 3. **Markov Decision Processes** - As you progress, you’ll learn to formulate real-world problems into the framework of Markov Decision Processes (MDPs). This is crucial, as the solution quality depends significantly on this translation. You'll also tackle different task types, enriching your problem-solving toolkit. 4. **Value Functions & Bellman Equations** - Understanding value functions and the well-known Bellman equations is next on the agenda. This week significantly deepens your grasp of how to compute optimal policies efficiently. 5. **Dynamic Programming** - Finally, you will learn how dynamic programming can be applied to reinforce learned behaviors. Implementing dynamic programming in real-world scenarios, such as industrial control problems, allows you to see the practical applications of what you've learned. **Recommendations** The "Fundamentals of Reinforcement Learning" is undeniably a valuable course for both beginners and those with some background in machine learning. Here’s why I recommend it: - **Engaging and Structured Content**: The course is well-organized, with each section building on the previous one. This scaffolded learning approach makes it easier to grasp complex concepts. - **Hands-on Experience**: The graded assessments encourage practical application of theory, reinforcing knowledge through implementation. You'll gain confidence in your abilities to create and test algorithms. - **Expert Instructors**: The expertise of your instructors is palpable throughout the course; they provide not only knowledge but also the necessary context that connects theory to practical applications. - **Industry Relevance**: As more companies look to incorporate AI-driven decision-making tools, understanding reinforcement learning will be a significant asset in the job market. This course equips you with the foundational skills required for that journey. - **Community Engagement**: The integration of Coursera’s community discussions fosters collaboration and learning from peers, enriching the overall educational experience. In conclusion, if you wish to step into the world of reinforcement learning and understand its applications better, enroll in "Fundamentals of Reinforcement Learning" on Coursera. You’ll leave not only with valuable knowledge but also with the skills needed to create intelligent agents capable of making automated decisions. Whether you're pursuing a career in AI or simply looking to broaden your knowledge, this course is a worthy investment in your future.

Syllabus

Welcome to the Course!

Welcome to: Fundamentals of Reinforcement Learning, the first course in a four-part specialization on Reinforcement Learning brought to you by the University of Alberta, Onlea, and Coursera. In this pre-course module, you'll be introduced to your instructors, get a flavour of what the course has in store for you, and be given an in-depth roadmap to help make your journey through this specialization as smooth as possible.

For the first week of this course, you will learn how to understand the exploration-exploitation trade-off in sequential decision-making, implement incremental algorithms for estimating action-values, and compare the strengths and weaknesses to different algorithms for exploration. For this week’s graded assessment, you will implement and test an epsilon-greedy agent.

When you’re presented with a problem in industry, the first and most important step is to translate that problem into a Markov Decision Process (MDP). The quality of your solution depends heavily on how well you do this translation. This week, you will learn the definition of MDPs, you will understand goal-directed behavior and how this can be obtained from maximizing scalar rewards, and you will also understand the difference between episodic and continuing tasks. For this week’s graded assessment, you will create three example tasks of your own that fit into the MDP framework.

Once the problem is formulated as an MDP, finding the optimal policy is more efficient when using value functions. This week, you will learn the definition of policies and value functions, as well as Bellman equations, which is the key technology that all of our algorithms will use.

This week, you will learn how to compute value functions and optimal policies, assuming you have the MDP model. You will implement dynamic programming to compute value functions and optimal policies and understand the utility of dynamic programming for industrial applications and problems. Further, you will learn about Generalized Policy Iteration as a common template for constructing algorithms that maximize reward. For this week’s graded assessment, you will implement an efficient dynamic programming agent in a simulated industrial control problem.