Neuromorphic Control for 3D Navigation in Minecraft Using Genetic Algorithms

2026-05-05 · Source: cs.NE updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Advanced, long

Summary

Researchers developed a genetic algorithm to train a neural network for autonomous 3D navigation in Minecraft: Java Edition, specifically for "parkour" challenges. The project uses the Mineflayer API to allow bots to interact with a Vanilla Minecraft server (version 1.19.4) and collect sensor data. The neural network, a single-hidden-layer multilayer perceptron with 32 neurons, receives 33 inputs from raycasting (19 rays for ground, ledge, ceiling detection, goal offset, velocity, on-ground flag, distance to goal, and an internal clock) and outputs four control actions: strafe left, strafe right, jump (binary), and mouse yaw delta (continuous). To overcome overfitting to fixed courses, the team implemented Continual Domain Randomization (CDR) using in-game command blocks to randomize obstacle layouts between generations, enabling the network to generalize to unseen balance beam-style obstacles.

Key takeaway

For research scientists developing autonomous navigation systems, this work demonstrates that training in a high-fidelity, randomized virtual environment like Minecraft can yield robust policies that generalize to novel scenarios. You should consider using in-game randomization techniques, such as command blocks, to create dynamic curricula that force agents to learn transferable skills rather than memorizing motor sequences, thereby improving Sim2Real transferability and reducing the risk of catastrophic forgetting.

Key insights

Genetic algorithms can train neural networks for complex 3D navigation in dynamic, randomized virtual environments.

Principles

• Sim2Real benefits from training in real game engines.
• Raycasting reduces input complexity for spatial tasks.
• Continual Domain Randomization prevents overfitting.

Method

A genetic algorithm evolves neural network weights for Minecraft bots. Bots receive raycast sensor data, and a fitness function rewards progression and penalizes unproductive behavior. Continual Domain Randomization randomizes obstacle layouts to promote generalization.

In practice

• Use Mineflayer API for Minecraft bot development.
• Implement raycasting for efficient spatial awareness.
• Apply CDR for robust policy generalization.

Topics

• Neuromorphic Control
• Minecraft Parkour
• Genetic Algorithms
• Neural Networks
• Simulation-to-Reality

Code references

• PrismarineJS/mineflayer
• cabaletta/baritone

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Robotics Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.NE updates on arXiv.org.