Genetic Algorithm with Neural Network Snake Game Agent
A completed game demo
Fitness/Score over Generations
Note: It's not the complete one. The environment went into a loop as I earlier I did not taken into account game completion. The Snakes were retrained for another 100+ generations and many started clearing the game.
Description
As part of learning the art of 'data science/machine learning/artificial intelligence', I embarked the journey to create an AI agent to play the classic Snake game. Using genetic algorithm to encourage evolution that ultimately convergences to optimal game outcome (full points).
Learning Journey
- Write Neural Network and Feed Forward (prediction engine) from down up
- Code genetic algorithm from scratch (and help from Chrispresso)
- Write the Snake environment/game itself
- Write test programs to validate the snake environment for correctness
- Write test programs to validate the Neural Network implementation
- Optimize to make the Snake environment 'play' or 'evaluate' the agent much faster (8x faster than original), and partially with the use of Numba
Credits
Concepts, neural network structure and fitness functions are based on Chrispresso's work https://chrispresso.io/AI_Learns_To_Play_Snake