Learn AI Game Development using Python

Course Structure and Topics

• Dynamic Programming (DP):

Introduction to DP: Understand the basic principles and applications of dynamic programming.

• Q-learning:

Fundamentals of Q-learning: Learn the theory behind Q-learning, a model-free RL algorithm.

Value Function and Policies: Understand how agents learn to map states to actions to maximize cumulative reward.

Implementation: Hands-on projects using TensorFlow and Keras to build and train Q-learning agents.

• Deep Q-learning:

Integrating Deep Learning with RL: Learn how deep neural networks can enhance Q-learning.

Handling High-dimensional Spaces: Techniques to manage complex environments and large state spaces.

Practical Projects: Implement deep Q-learning models to solve more sophisticated problems.

• Convolutional Q-learning:

Combining CNNs with Q-learning: Utilize convolutional neural networks to process spatial and visual data.

Advanced Applications: Implement RL in environments where visual perception is crucial, such as video games and robotics.

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Exciting Projects

To bring these concepts to life, we’ll be implementing a series of exciting projects:

• Maze Solver: Program an agent to find the shortest path through a maze, applying principles of DP and RL.
• Mountain Car Problem: Tackle this classic RL challenge where an agent must drive a car up a steep hill using momentum.
• Snake Game: Develop a snake game where the agent learns to maximize its length while avoiding obstacles and navigating the game board efficiently.

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Tools and Libraries

Throughout the course, we’ll be using TensorFlow and Keras to build and train our models. These libraries provide a robust framework for developing machine learning applications, making it easier to implement and experiment with the algorithms we’ll be studying.

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