Udemy MOOC / Non-credit 0

AI Learning to Play Tom & Jerry: Reinforcement Q-Learning

Name: AI Learning to Play Tom & Jerry: Reinforcement Q-Learning
Price: 19.99 USD
Availability: InStock

About this course

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. Y

62/100

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What the provider tells you: 38/45
Who stands behind it: 8/35
How complete the listing is: 16/20

Scores how much the provider publishes and who stands behind it — not how well it is taught.

What you'll learn

understanding reinforcement Q-learning
implementing a Q-learning agent in Python
creating game elements using the Turtle library
defining state and action spaces
training agents with Q-learning
analyzing agent performance
fine-tuning hyperparameters for improved learning

Machine Learning #reinforcement learning #python #machine learning #game development #hyperparameter tuning #Q-learning #exploration-exploitation #reward shaping #agent-based modeling #turtle graphics

$19.99

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