dino ggl game Design: Before coding, I will create a blueprint of the dino game automator. I will determine the key components and features that the game should have, such as collision detection, obstacle generation, score tracking, and dino character movement.
Code: Once the design is complete, I will begin coding the dino game automator using Python. I will use libraries like pygame and numpy to handle the game logic, character movement, and user inputs.
Integration: To integrate the AI component, I will utilize the PyTorch library to train a neural network to make decisions based on the current game state. This AI model will learn to recognize patterns in the game, allowing it to predict when to jump over obstacles.
Testing: To ensure the dino game automator is functioning correctly, I will conduct thorough testing and debugging. This will involve verifying the accuracy of the AI model, as well as addressing any errors or bugs that arise during the development process.
Documentation: Once the dino game automator is fully functional, I will thoroughly document the project, including a detailed description of the AI model and its training process.
Deployment: To share the dino game automator with others, I will upload the project files to a GitHub repository. By making the repository public, I will enable others to view, clone, and contribute to the project.