AI Learning to Play Tom & Jerry: Reinforcement Q-Learning
Master Reinforcement Learning with Tom and Jerry: Build a Q-Learning Game
5.00 (2 reviews)
55
students
2 hours
content
May 2024
last update
$13.99
regular price
What you will 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.
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5999426
udemy ID
30/05/2024
course created date
14/06/2024
course indexed date
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