Two-Bit Direction Encoding Delivers 6.4x Memory Efficiency in Snake AI

This article demonstrates how reconsidering data representation can yield dramatic performance gains in AI systems. By replacing an intuitive but flawed multi-channel direction encoding with a geometrically-grounded two-bit approach, the author reduced memory consumption by 6.4x—proving that clever data design often outperforms algorithmic complexity in resource-constrained applications.