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

AI Learning to Play Tom & Jerry: Reinforcement Q-Learning, available at $54.99, has an average rating of 5, with 19 lectures, based on 2 reviews, and has 50 subscribers.

You will learn about 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. This course is ideal for individuals who are Beginners in machine learning who want to delve into reinforcement learning. or Python developers interested in expanding their skills to include Q-learning. or Game developers who want to incorporate intelligent agents into their games. or Students or professionals looking to gain hands-on experience with reinforcement learning in a practical project. or Individuals interested in understanding the concepts and applications of Q-learning through a fun and interactive game. It is particularly useful for Beginners in machine learning who want to delve into reinforcement learning. or Python developers interested in expanding their skills to include Q-learning. or Game developers who want to incorporate intelligent agents into their games. or Students or professionals looking to gain hands-on experience with reinforcement learning in a practical project. or Individuals interested in understanding the concepts and applications of Q-learning through a fun and interactive game.

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

Summary

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

Price: $54.99

Average Rating: 5

Number of Lectures: 19

Number of Published Lectures: 19

Number of Curriculum Items: 19

Number of Published Curriculum Objects: 19

Original Price: $19.99

Quality Status: approved

Status: Live

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.

Who Should Attend

• 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.

Target Audiences

• 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.