Master Reinforcement Learning -Markov Decision Process (MDP)
Mastering Reinforcement Learning: From Gridworld to Real-World Applications
Lectures -9
Resources -1
Duration -2 hours
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Course Description
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.
Goals
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
Prerequisites
Basic programming skills in Python

Curriculum
Check out the detailed breakdown of what’s inside the course
Introduction
1 Lectures
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Introduction 00:56 00:56
Course Content
8 Lectures

Instructor Details

Abdurrahman Tekin
Abdurrahman Tekin is a passionate academic and educator driven by a deep fascination with cutting-edge technologies and a commitment to sharing knowledge. Currently pursuing his Ph.D. at the prestigious Nanjing University of Aeronautics and Astronautics, Abdurrahman's research delves into the captivating realm of "Multi-Objective Airfoil/Wing Shape Optimization using Deep Learning, Bayesian methods, and Knowledge-Based Modeling."
With a profound understanding of artificial intelligence, programming, and language learning, Abdurrahman has embarked on a mission to empower learners worldwide through his online teaching endeavors. As an esteemed instructor on Tutorialspoint, he has successfully guided over 50,000 students from 166 different countries, imparting invaluable skills in AI, Python, English, and Chinese.
Beyond the virtual classroom, Abdurrahman's enthusiasm for education extends to his YouTube channel, where he shares his experiences and insights with a growing community of over 8,000 followers. Through engaging videos, he provides a unique glimpse into his academic journey and offers practical advice to aspiring learners.
Abdurrahman's multifaceted approach to education reflects his unwavering commitment to lifelong learning and his belief in the transformative power of knowledge. With a unique blend of academic rigor and a passion for teaching, he continues to inspire and empower individuals across the globe, paving the way for a future where innovation and education go hand in hand.
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