arxiv: 2605.02628 · v1 · submitted 2026-05-04 · 💻 cs.NE

Recognition: unknown

Neuromorphic Control for 3D Navigation in Minecraft Using Genetic Algorithms

Eric Zipor

Authors on Pith no claims yet

Pith reviewed 2026-05-08 01:44 UTC · model grok-4.3

classification 💻 cs.NE

keywords genetic algorithmsneural networksMinecraftpathfinding3D navigationevolutionary computationgame AIparkour

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The pith

A genetic algorithm evolves neural network weights to control navigation through Minecraft parkour maps.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper describes a genetic algorithm that produces weights for a neural network. The network receives inputs on block distances, terrain features, and obstacles and outputs movement decisions for 3D pathing. This setup aims to let an agent traverse parkour challenges efficiently without hand-coded rules. A reader would care if the method shows that evolutionary search can discover controllers suited to precise timing and physics in voxel environments. Success would mean the network generalizes path choices across different map layouts.

Core claim

The authors 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.

What carries the argument

Genetic algorithm that evolves weights for a neural network whose inputs are distances to blocks, terrain type, and obstacle locations.

Load-bearing premise

A fitness function based on path efficiency and success rate will produce weights that work on new maps instead of only on the ones used for training.

What would settle it

Run the evolved network on a set of Minecraft parkour maps never seen during training and measure whether it completes them at the same success rate and speed as on training maps.

Figures

Figures reproduced from arXiv: 2605.02628 by Eric Zipor.

**Figure 1.** Figure 1: Parallel generation of bots navigating a randomized string of blocks view at source ↗

**Figure 2.** Figure 2: Example of occlusion detection via voxel status using ray casting view at source ↗

**Figure 4.** Figure 4: 90 degree bend obstacle (upper left), jump-around-pillar, community view at source ↗

**Figure 3.** Figure 3: Example flow diagram of reinforcement learning methodology [5]. view at source ↗

read the 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

This is a high-level sketch of using genetic algorithms to evolve neural weights for Minecraft parkour navigation, with no experiments, results, or implementation details to support the claims.

read the letter

The main thing here is that the paper describes a plan to train a neural network controller for Minecraft parkour using a genetic algorithm, but it provides no runs, metrics, or code to show whether the idea actually works. The setup takes inputs like block distances, terrain, and obstacles, then uses the GA to search for weights that produce good action sequences. This is a standard evolutionary robotics pattern applied to a game with tricky 3D physics and timing demands. The choice of Minecraft parkour as the testbed makes sense because the environment forces the controller to handle jumps, sprinting, and obstacle avoidance in a voxel world that many people already know. The input encoding sounds reasonable on paper for capturing the key state information. Beyond that, there is not much new. The approach follows textbook methods from evolutionary computation and game AI without introducing new operators, representations, or theoretical insights. The real problem is the missing evidence. There are no details on network size or layers, no precise fitness function, no GA hyperparameters, and zero performance numbers from any maps. The claim of determining the most optimal pathing therefore sits unsupported. Without at least success rates, comparisons to random or scripted controllers, or fitness curves, it is impossible to judge if the evolved system generalizes or even completes basic jumps. This reads like an initial project outline rather than a finished study. It could interest students or hobbyists who want to try similar setups in games, but it does not give researchers in control or evolutionary methods anything concrete to build on or cite. I would not bring it to a reading group or reference it in my own work. It does not deserve peer review until the authors add actual implementation, runs on multiple maps, and measurable results.

Referee Report

2 major / 1 minor

Summary. The manuscript claims to design a genetic algorithm that evolves weights for a neural network controller. The controller takes inputs on block distances, terrain, and obstacles in Minecraft to autonomously determine the most optimal pathing for parkour navigation.

Significance. If the approach were fully implemented with reproducible results showing reliable navigation success across maps, it could contribute to evolutionary neuromorphic control and game AI by demonstrating GA-driven adaptation in a physics-rich 3D environment. The idea aligns with interests in parameter-light controllers for complex tasks, but the current text provides no evidence to evaluate this potential.

major comments (2)

[Abstract] Abstract: The assertion that the system determines 'the most optimal pathing' is unsupported. The text contains no fitness function, no neural network architecture or input encoding details, no evolved weights, no success rates, no fitness curves, and no comparisons against baselines or random controllers.
[Full Text] Full manuscript: No experimental validation is reported on even a single parkour map, no hyperparameters for the genetic algorithm are specified, and no discussion of generalization or overfitting risks appears, leaving the central claim as an unverified design outline.

minor comments (1)

The submission appears to consist only of the abstract; if additional sections exist they must be included to allow evaluation.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the careful reading and for highlighting the gaps in our manuscript. We agree that the current version presents an unverified design outline rather than a complete empirical study, and the abstract overstates the results. We will revise the manuscript accordingly to include the missing details, experimental validation, and tempered claims.

read point-by-point responses

Referee: [Abstract] Abstract: The assertion that the system determines 'the most optimal pathing' is unsupported. The text contains no fitness function, no neural network architecture or input encoding details, no evolved weights, no success rates, no fitness curves, and no comparisons against baselines or random controllers.

Authors: We agree that the abstract's phrasing is unsupported and will revise it to describe the work as a proposed genetic-algorithm method for evolving a neural-network controller rather than claiming optimality. In the revision we will add the fitness function definition, neural-network architecture and input encoding, representative evolved weights, success rates on test maps, fitness curves, and comparisons against random and baseline controllers. revision: yes
Referee: [Full Text] Full manuscript: No experimental validation is reported on even a single parkour map, no hyperparameters for the genetic algorithm are specified, and no discussion of generalization or overfitting risks appears, leaving the central claim as an unverified design outline.

Authors: The referee is correct; the submitted manuscript contains only the design description and no experiments, hyperparameters, or generalization analysis. We will add a new experimental section that reports results on multiple parkour maps, lists all genetic-algorithm hyperparameters, and discusses generalization and overfitting mitigation strategies such as map diversity and held-out validation sets. revision: yes

Circularity Check

0 steps flagged

No circularity detected; paper is a high-level design outline with no equations or derivations

full rationale

The manuscript presents a high-level description of using a genetic algorithm to evolve neural-network weights for Minecraft navigation based on block distances, terrain, and obstacles. No equations, fitted parameters, predictions, self-citations, uniqueness theorems, or ansatzes are present. The central claim is a design statement rather than a derived result that could reduce to its inputs by construction. The derivation chain is empty, making the work self-contained as a method proposal.

Axiom & Free-Parameter Ledger

3 free parameters · 1 axioms · 0 invented entities

The central claim rests on standard assumptions of evolutionary computation and neural networks without introducing new free parameters, axioms, or entities beyond those implicit in any GA-NN setup.

free parameters (3)

neural network architecture and input encoding
Number of layers, neurons, and how block distances/terrain are represented as inputs must be chosen by hand.
genetic algorithm hyperparameters
Population size, mutation rate, crossover method, and number of generations are free parameters required for any such run.
fitness function definition
How success, path length, and timing are quantified to score individuals is not specified and must be designed.

axioms (1)

domain assumption Genetic algorithms can converge to useful weights for continuous control tasks given sufficient generations and a well-shaped fitness landscape.
Invoked implicitly by the design of the method; no proof or reference supplied.

pith-pipeline@v0.9.0 · 5388 in / 1264 out tokens · 28499 ms · 2026-05-08T01:44:54.922942+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

8 extracted references · 4 canonical work pages

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