Binary Plane Encoding Doubles Snake AI Performance Records

Previous Snake AI research faced a fundamental trade-off: hand-crafted feature approaches sacrifice spatial pattern learning for efficiency, while pixel-based approaches preserve spatial information at massive computational cost. The new Binary Plane Encoding method eliminates this trade-off by efficiently encoding grid states while preserving spatial relationships, achieving 125 points on a 20×20 grid—double the previous best published score of 62. This breakthrough demonstrates that intermediate representation approaches can simultaneously improve performance, computational efficiency, and generalizability across different environments.