AI Learning to Play Tom & Jerry: Reinforcement Q-Learning
Master Reinforcement Learning with Tom and Jerry: Build a Q-Learning Game
Data Science and AI ML,ML,Deep Learning,Python,Python Programming
Lectures -19
Resources -1
Duration -2 hours
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Course Description
Learn Reinforcement Q-Learning by creating a fun and interactive "Tom and Jerry" game project! In this comprehensive course, you will dive into the world of reinforcement learning and build a Q-learning agent using Python and the Turtle graphics library.
Reinforcement Q-Learning is a popular approach in machine learning that enables an agent to learn optimal actions in an environment through trial and error. By implementing this algorithm in the context of the classic "Tom and Jerry" game, you will gain a deep understanding of how Q-learning works and how it can be applied to solve real-world problems.
Throughout the course, you will be guided step-by-step in developing the game project. You will start by setting up the game screen using the Turtle library and creating the game elements, including the Tom and Jerry characters. Next, you will define the state space and action space, which will serve as the foundation for the Q-learning algorithm.
The course will cover important concepts such as reward shaping, discount factor, and exploration-exploitation trade-off. You will learn how to train the prey (Jerry) and predator (Tom) agents using Q-learning, updating their Q-tables based on the rewards and future expected rewards. By iteratively updating the Q-tables, the agents will learn optimal actions to navigate the game environment and achieve their goals.
Throughout the course, you will explore various scenarios and challenges, including avoiding obstacles, reaching the target turtle, and optimizing the agents' strategies. You will analyze the agents' performance and observe how their Q-tables evolve with each training iteration. Additionally, you will learn how to fine-tune the hyperparameters of the Q-learning algorithm to improve the agents' learning efficiency.
By the end of this course, you will have a solid understanding of Reinforcement Q-Learning and how to apply it to create intelligent agents in game environments. You will have hands-on experience with Python, Turtle graphics, and Q-learning algorithms. Whether you are a beginner in machine learning or an experienced practitioner, this course will enhance your skills and empower you to tackle complex reinforcement learning problems.
Enroll now and embark on an exciting journey to master Reinforcement Q-Learning through the "Tom and Jerry" game project! Let's train Tom and Jerry to outsmart each other and achieve their objectives in this dynamic and engaging learning experience.
Goals
The fundamentals of Reinforcement Q-Learning.
How to create a "Tom and Jerry" game using Python and Turtle graphics.
Setting up the game screen and creating game elements.
Defining the state space and action space for the Q-learning algorithm.
Reward shaping and its role in reinforcement learning.
The concept of discount factor and its impact on future rewards.
Balancing exploration and exploitation in the Q-learning process.
Prerequisites
Basic knowledge of Python programming language.
Familiarity with fundamental programming concepts (variables, loops, conditionals, functions).
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Curriculum
Check out the detailed breakdown of what’s inside the course
Introduction
1 Lectures
-
Introduction 00:40 00:40
Course Content
18 Lectures
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Instructor Details
![Abdurrahman Tekin](https://www.tutorialspoint.com/assets/profiles/448556/profile/200_2353137-1649516080.png)
Abdurrahman Tekin
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