Neuromorphic Control for 3D Navigation in Minecraft Using Genetic Algorithms
TLDR
This paper uses a genetic algorithm to train a neural network for autonomous 3D navigation and parkour in Minecraft.
Key contributions
- Develops a genetic algorithm to optimize neural network weights.
- Enables autonomous 3D navigation and 'parkour' in Minecraft.
- Neural network evaluates block distances, terrain, and obstacles for optimal pathing.
- Addresses complex motion mechanics like sprinting and sneaking for difficult jumps.
Why it matters
This research demonstrates an innovative application of AI for complex in-game navigation challenges. It offers insights into training agents for precise, real-time control in dynamic 3D environments. The approach could extend to other robotics or simulation tasks.
Original Abstract
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.
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