Master Reinforcement Learning -Markov Decision Process (MDP)

Master Reinforcement Learning -Markov Decision Process (MDP), available at $54.99, has an average rating of 5, with 9 lectures, based on 3 reviews, and has 73 subscribers.

You will learn about Foundations of Markov Decision Processes Modeling real-world problems as MDPs Defining state spaces, action spaces, and transition probabilities Constructing reward functions for different objectives Value iteration algorithm for computing optimal value functions Solving gridworld navigation problems with obstacles Robotic navigation and path planning using MDPs This course is ideal for individuals who are Students and researchers in the field of artificial intelligence or Data scientists and machine learning engineers or Operations research professionals and decision analysts or Quantitative analysts in finance and portfolio management or Robotics engineers and autonomous system developers It is particularly useful for Students and researchers in the field of artificial intelligence or Data scientists and machine learning engineers or Operations research professionals and decision analysts or Quantitative analysts in finance and portfolio management or Robotics engineers and autonomous system developers.

Enroll now: Master Reinforcement Learning -Markov Decision Process (MDP)

Summary

Title: Master Reinforcement Learning -Markov Decision Process (MDP)

Price: $54.99

Average Rating: 5

Number of Lectures: 9

Number of Published Lectures: 9

Number of Curriculum Items: 9

Number of Published Curriculum Objects: 9

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

Foundations of Markov Decision Processes
Modeling real-world problems as MDPs
Defining state spaces, action spaces, and transition probabilities
Constructing reward functions for different objectives
Value iteration algorithm for computing optimal value functions
Solving gridworld navigation problems with obstacles
Robotic navigation and path planning using MDPs

Who Should Attend

Students and researchers in the field of artificial intelligence
Data scientists and machine learning engineers
Operations research professionals and decision analysts
Quantitative analysts in finance and portfolio management
Robotics engineers and autonomous system developers

Target Audiences

Students and researchers in the field of artificial intelligence
Data scientists and machine learning engineers
Operations research professionals and decision analysts
Quantitative analysts in finance and portfolio management
Robotics engineers and autonomous system developers

In today’s complex world, making optimal decisions is a critical skill for success in various domains, from robotics and automation to finance and resource management. This course will equip you with the power of Markov Decision Processes (MDPs), a fundamental framework for sequential decision-making under uncertainty.

Through a series of hands-on, real-life projects, you’ll learn how to model and solve challenging decision-making problems using MDPs. You’ll start by exploring the foundations of MDPs, including state spaces, action spaces, transition probabilities, and reward functions. With these building blocks, you’ll construct realistic scenarios, such as navigating a robot through an environment with obstacles, optimizing portfolio management strategies, or planning efficient resource allocation in supply chains.

As you progress, you’ll dive into advanced MDP techniques, including value iteration. You’ll master the art of computing optimal value functions and deriving optimal policies that maximize long-term rewards. Additionally, you’ll learn how to handle partial observability, continuous state and action spaces, and other real-world complexities.

But this course goes beyond theory. Through immersive projects, you’ll gain practical experience in implementing MDPs using Python and powerful libraries like NumPy. You’ll tackle gridworld environments, robotic navigation challenges, and even complex financial decision-making scenarios, all while honing your problem-solving skills and developing a deep understanding of MDP applications.

By the end of this course, you’ll have a solid grasp of MDP concepts and a portfolio of projects that demonstrate your ability to model and solve intricate decision-making problems. Whether you’re a student, researcher, or professional in fields like AI, operations research, or finance, this course will empower you to make informed, intelligent decisions that drive success in your domain.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Chapter 2: Course Content

Lecture 1: 1 Value Iteration for Markov Decision Process

Lecture 2: 2 MDP Value Iteration Algorithm

Lecture 3: 3 Using Iteration to converge

Lecture 4: 4 Grid World MDP Example

Lecture 5: 5 Grid World Value Iteration

Lecture 6: 6 Finding the optimal path from start to end state

Lecture 7: 7 Finding the optimal path between walls7 Finding the optimal path between walls

Lecture 8: 8 Solving Labyrinth problem

Instructors

Abdurrahman TEKIN
PhD student

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5 stars: 3 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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