docs: Add an example of a fully GPU genetic algorithm#2250
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docs: Add an example of a fully GPU genetic algorithm#2250
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Pull request overview
This PR adds a new interactive example demonstrating a fully GPU-driven genetic algorithm for 2D car racing simulation. Cars navigate procedurally generated tracks using sensor-based inputs processed through evolved genomes, with tournament selection, crossover, and mutation all running in GPU compute shaders.
Changes:
- Added a complete genetic racing example with procedural track generation (
track.ts), GPU-based genetic algorithm logic (ga.ts), and the main simulation/rendering loop (index.ts) - Included interactive controls for grid size, population, mutation parameters, and simulation speed, along with a car sprite asset and HTML overlay for stats display
- Added example metadata (
meta.json) and thumbnail for the docs example gallery
Reviewed changes
Copilot reviewed 5 out of 7 changed files in this pull request and generated no comments.
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| File | Description |
|---|---|
apps/typegpu-docs/src/examples/algorithms/genetic-racing/meta.json |
Example metadata with title, category, and tags |
apps/typegpu-docs/src/examples/algorithms/genetic-racing/index.html |
Canvas element with stats overlay div and styling |
apps/typegpu-docs/src/examples/algorithms/genetic-racing/index.ts |
Main example: GPU pipeline setup, simulation loop, rendering, and user controls |
apps/typegpu-docs/src/examples/algorithms/genetic-racing/ga.ts |
Genetic algorithm: data schemas, init/evolve compute shaders, population management |
apps/typegpu-docs/src/examples/algorithms/genetic-racing/track.ts |
Procedural track generation with random walk paths and Catmull-Rom spline smoothing |
apps/typegpu-docs/public/assets/genetic-car/car.png |
Car sprite image asset |
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| if (hasChampion) { | ||
| const src = root.unwrap(championGenomeBuffer); | ||
| const encoder = root.device.createCommandEncoder(); | ||
| encoder.copyBufferToBuffer(src, 0, root.unwrap(ga.genomeBuffers[ga.current]), 0, src.size); | ||
| root.device.queue.submit([encoder.finish()]); | ||
| } |
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I'm not a fan of this but I think i prefer it to dispatching an entire compute shader to copy a couple of bytes
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Pull request overview
Copilot reviewed 5 out of 7 changed files in this pull request and generated 2 comments.
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apps/typegpu-docs/src/examples/algorithms/genetic-racing/track.ts
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apps/typegpu-docs/src/examples/algorithms/genetic-racing/index.ts
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Split Genome into InputLayer, DenseLayer and OutputLayer and replace flat weight vectors with mat4x4 matrices. Add FitnessArray and a fit compute shader plus a reduction to select the champion; wire the new fitness buffer into init/evolve flows. Update mutation/evolution helpers and simulation to evaluate the layered network and use the reworked sensor inputs.
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
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Pull request overview
Copilot reviewed 7 out of 9 changed files in this pull request and generated 1 comment.
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apps/typegpu-docs/src/examples/algorithms/genetic-racing/index.ts
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