AI Learning to Play Tom & Jerry Reinforcement Q-Learning

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Free Download AI Learning to Play Tom & Jerry Reinforcement Q-Learning
Published 6/2024
Created by Abdurrahman TEKIN
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 19 Lectures ( 2h 13m ) | Size: 1.84 GB

Master Reinforcement Learning with Tom and Jerry: Build a Q-Learning Game
What you'll learn:
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.
Training the prey (Jerry) and predator (Tom) agents using Q-learning.
Updating the Q-tables based on rewards and expected future rewards.
Analyzing agent performance and observing Q-table evolution.
Handling obstacles and reaching target objectives in the game environment.
Fine-tuning hyperparameters to enhance learning efficiency.
Gaining hands-on experience with Python programming and Turtle graphics.
Developing problem-solving and algorithmic thinking skills.
Requirements:
Basic knowledge of Python programming language.
Familiarity with fundamental programming concepts (variables, loops, conditionals, functions).
Description:
Learn Reinforcement Q-Learning by creating a fun and interactive "Tom and Jerry" game project! In this comprehensive Udemy 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.
Who this course is for:
Beginners in machine learning who want to delve into reinforcement learning.
Python developers interested in expanding their skills to include Q-learning.
Game developers who want to incorporate intelligent agents into their games.
Students or professionals looking to gain hands-on experience with reinforcement learning in a practical project.
Individuals interested in understanding the concepts and applications of Q-learning through a fun and interactive game.
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