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Master Reinforcement Learning -Markov Decision Process (MDP)

person icon Abdurrahman Tekin

4.6

Master Reinforcement Learning -Markov Decision Process (MDP)

Mastering Reinforcement Learning: From Gridworld to Real-World Applications

updated on icon Updated on Jun, 2024

language icon Language - English

person icon Abdurrahman Tekin

category icon Data Science and AI ML,Data Analysis,Machine Learning

Lectures -9

Resources -1

Duration -2 hours

4.6

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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

Master Reinforcement Learning -Markov Decision Process (MDP)

Curriculum

Check out the detailed breakdown of what’s inside the course

Introduction
1 Lectures
  • play icon Introduction 00:56 00:56
Course Content
8 Lectures
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Instructor Details

Abdurrahman Tekin

Abdurrahman Tekin


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