Pong Cloned by Neural Network

2025-08-24 · Source: neural network – Hackaday · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Intermediate, quick

Summary

Nick Bild has recreated the classic arcade game Pong using a neural network, departing from traditional hardwired logic. This project involved training a transformer-based network on hundreds of thousands of gameplay images, supplemented by synthetic data for infrequent events like paddle misses. The network features distinct branches for predicting ball movement, processing user input, and forecasting paddle motion, with a final branch integrating these predictions. The system operates by receiving four initial frames and subsequently predicting all game elements. Notably, the neural network was trained solely on two old Xeon processors due to the absence of a GPU, yet the resulting game performance is visually indistinguishable from a conventionally coded version.

Key takeaway

For AI engineers exploring alternative game development or resource-constrained training, this project demonstrates that a transformer-based neural network can effectively replicate game logic using image data, even when trained on older CPU hardware. You should consider generating synthetic data for rare game states to ensure comprehensive model training, potentially enabling complex AI applications without requiring high-end GPUs.

Key insights

A neural network can replicate classic game logic using transformer architecture and image-based training.

Principles

• Synthetic data augments real data for rare events.
• Branching networks can handle distinct prediction tasks.

Method

Train a transformer-based neural network with separate branches for ball, user input, and paddle predictions, using real and synthetic gameplay images, then integrate outputs.

In practice

• Replicate simple game logic with neural networks.
• Train complex models on CPU-only hardware.

Topics

• Neural Networks
• Transformer Models
• Game AI
• Synthetic Data
• Low-Resource AI

Best for: AI Engineer, Machine Learning Engineer, AI Student

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Editorial summary, takeaway, and curation by AIssential. Original article published by neural network – Hackaday.