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Reinforcement Q-Learning: Build Turtle-Controlled AI Agent

person icon Abdurrahman Tekin

4.6

Reinforcement Q-Learning: Build Turtle-Controlled AI Agent

Dive into Reinforcement Learning with Q-Learning, Reinforcement Learning with Turtles: A Hands-On Q-Learning Journey

updated on icon Updated on Jul, 2024

language icon Language - English

person icon Abdurrahman Tekin

category icon Python,Python Programming,Data Science and AI ML,ML,Deep Learning

Lectures -10

Resources -1

Duration -2.5 hours

4.6

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Course Description

Dive into the captivating world of Reinforcement Learning and master the art of Q-Learning through a thrilling game-based project involving turtles. In this comprehensive course, you'll embark on an engaging journey to build your own AI-controlled turtle agent that navigates a dynamic maze, learning to make optimal decisions and achieve its goals.


Reinforcement Learning is a powerful technique that allows agents (like our turtle) to learn and improve their behavior through trial-and-error interactions with their environment. By implementing the Q-Learning algorithm, you'll witness firsthand how an agent can learn to make the best decisions to maximize its rewards and successfully reach its objectives.


Throughout the course, you'll:


- Understand the fundamental principles of Reinforcement Learning and the Q-Learning algorithm

- Implement the Q-Learning algorithm from scratch, using Python and the Turtle graphics library

- Design a dynamic maze environment with obstacles, target locations, and a turtle agent

- Train your turtle agent to navigate the maze and reach its goals using the Q-Learning technique

- Visualize the learning progress and analyze the agent's performance over time

- Explore techniques to optimize the Q-Learning process, such as adjusting the learning rate and exploration-exploitation tradeoff

- Gain valuable insights into the practical applications of Reinforcement Learning in real-world scenarios


By the end of this course, you'll have a solid understanding of Reinforcement Learning and the Q-Learning algorithm, as well as the skills to apply these concepts to solve complex problems. Whether you're a beginner or an experienced programmer, this course will equip you with the knowledge and hands-on experience to become a proficient Reinforcement Learning practitioner.


Enroll now and embark on an exciting journey to master Reinforcement Learning through the captivating world of turtles!

Goals

Mastering Reinforcement Learning Fundamentals

Implementing the Q-Learning Algorithm

Designing Intelligent Agent Behavior

Navigating Complex Environments with Turtles

Optimizing Decision-Making Strategies

Visualizing and Interpreting Q-Learning Outputs

Applying Reinforcement Learning to Real-World Problems

Troubleshooting and Optimizing Q-Learning Models

Integrating Reinforcement Learning with Turtle Graphics

Prerequisites

Basic Python Programming

Familiarity with Turtle Graphics

Reinforcement Q-Learning: Build Turtle-Controlled AI Agent

Curriculum

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

Introduction
1 Lectures
  • play icon Introduction 01:08 01:08
Course Content
9 Lectures
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Instructor Details

Abdurrahman Tekin

Abdurrahman Tekin


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