Neuromorphic Control for 3D Navigation in Minecraft Using Genetic Algorithms
For game AI researchers, it demonstrates a neuroevolution approach to 3D navigation in a physics-rich environment, though results are qualitative and incremental.
This work uses a genetic algorithm to evolve neural network weights for controlling a Minecraft agent in parkour-style navigation, achieving autonomous pathing through complex terrain.
The popular 2009 voxel based videogame, Minecraft, contains several distinct disciplines. One of which is "parkour," gameplay that focuses on traversing a world's environment with maximum efficiency. The Minecraft online community has turned the game's physics engine into dynamic puzzles, requiring players to masterfully manipulate motion mechanics through frame precise timing of keystrokes. Actions such as sprinting, sneaking, and mouse direction are all combined to clear specific difficult jumps. Through this project, we design a genetic algorithm to generate weights for a neural network to autonomously evaluate inputs for block distances, terrain, and obstacles to determine the most optimal pathing.