Welcome to the exciting world of machine learning where a little bird learns to fly! In this article, we will guide you through creating a program that implements a machine learning algorithm in the Flappy Bird game. By utilizing neural networks and genetic algorithms, our program teaches the bird how to flap optimally and navigate through obstacles.

Understanding the Basics

Before we dive into the code, let’s set the stage with a quick analogy. Imagine teaching a child how to ride a bicycle. Initially, the child may wobble and fall several times, but with each attempt, they learn what to do and what not to do, refining their skills. Similarly, in our program, the bird learns to flap at the right time based on feedback from its environment, gradually mastering the game through repetition and optimization.

How to Build the Game

To begin creating our Flappy Bird machine learning implementation, follow these steps:

1. Set Up Your Environment

• Ensure you have the following files in your project:
• phaser.min.js – The game framework.
• synaptic.min.js – The library for the neural network.

2. Understand the Neural Network Architecture

Each bird in the game is represented by a neural network with three main layers:

• Input Layer: Contains 2 neurons:
  • Horizontal distance to the closest gap.
  • Height difference between the bird and the closest gap.
• Hidden Layer: Consists of 6 neurons.
• Output Layer: Contains 1 neuron that decides:
  • If the output is greater than 0.5, the bird flaps; otherwise, it does nothing.

3. Implementing the Genetic Algorithm

The main concept for training the bird uses a genetic algorithm, resembling the process of natural selection. Here’s how it works:

1. Create an initial population of 10 birds, each with a random neural network.
2. Let all birds play simultaneously.
3. Evaluate each bird’s fitness based on the distance traveled minus the distance to the nearest gap.
4. Once the birds “die,” the top performers are chosen to create a new generation through selection, crossover, and mutation.

Key Files and Logic

Gameplay Logic in gameplay.js

The game logic resides here, including the following functions:

• _preload()_: Preloads all game assets.
• _create()_: Initializes game objects and the genetic algorithm.
• _update()_: Handles the game loop where AI plays the game.
• _drawStatus()_: Displays ongoing status information for all units.

Genetic Algorithm in genetic.js

The genetic algorithm logic is encapsulated here:

• GeneticAlgorithm Class: Manages population dynamics, including:
• _reset()_: Resets parameters.
• _createPopulation()_: Creates a new bird population.
• _evolvePopulation()_: Evolves the population using genetic operators.

Troubleshooting Tips

If you encounter issues while running the Flappy Bird machine learning application, consider the following:

• Check Dependencies: Ensure both Phaser and Synaptic libraries are correctly linked in your HTML file.
• Configuration Errors: Review code for any configuration mistakes in your neural network setup.
• Performance Issues: Test your implementation in various browsers and devices.

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Conclusion

This tutorial provides a glimpse into the intersection of gaming and AI, showcasing how machines can learn through experience. At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.