Neuromorphic Control for 3D Navigation in Minecraft Using Genetic Algorithms

Problem
This paper addresses the gap in autonomous navigation within complex environments, specifically in the context of the voxel-based game Minecraft. The authors highlight the challenge of efficiently traversing intricate terrains, particularly in the “parkour” discipline, where players must execute precise movements to overcome obstacles. The work is presented as a preprint and has not undergone peer review, indicating that the findings should be interpreted with caution.

Method
The core technical contribution is the development of a genetic algorithm (GA) that optimizes the weights of a neural network designed for pathfinding in Minecraft. The neural network evaluates various inputs, including block distances, terrain types, and obstacles, to determine optimal navigation strategies. The GA iteratively evolves the neural network’s weights based on performance metrics, allowing for the adaptation of the model to the dynamic challenges presented by the game environment. The training process leverages a simulation of Minecraft’s physics engine, although specific details regarding the compute resources and training duration are not disclosed.

Results
The proposed method demonstrates significant improvements in navigation efficiency compared to baseline approaches, such as rule-based pathfinding algorithms. The authors report that their genetic algorithm-enhanced neural network achieves a 30% reduction in average traversal time across a set of predefined parkour challenges in Minecraft. Additionally, the model exhibits a 25% increase in successful jump completions compared to traditional heuristic methods. These results suggest that the genetic algorithm effectively enhances the neural network’s ability to adapt to complex terrain features.

Limitations
The authors acknowledge several limitations in their work. Firstly, the reliance on a genetic algorithm may lead to suboptimal solutions due to the stochastic nature of evolution, potentially resulting in local minima. Secondly, the model’s performance is evaluated solely within the Minecraft environment, limiting its generalizability to other domains or real-world applications. Furthermore, the paper does not address the computational overhead associated with the genetic algorithm, which may hinder scalability for larger or more complex environments. Lastly, the lack of a comprehensive comparison with state-of-the-art reinforcement learning methods is noted, which could provide a more robust evaluation of the proposed approach.

Why it matters
This research has significant implications for the field of autonomous navigation and robotics, particularly in environments characterized by complex and dynamic obstacles. By leveraging genetic algorithms for neural network optimization, the work opens avenues for further exploration of evolutionary strategies in machine learning. The findings could inform the development of more sophisticated pathfinding algorithms applicable to real-world scenarios, such as robotic navigation in unstructured environments. Additionally, the integration of game-based simulations like Minecraft as testbeds for AI research highlights the potential for using virtual environments to advance the understanding of complex decision-making processes.

Authors: Eric Zipor
Source: arXiv:2605.02628
URL: https://arxiv.org/abs/2605.02628v1