How Novelty Drives an RL Web Crawler
The most dangerous assumption in applied Reinforcement Learning (RL) is that useful exploration requires massive scale—cloud GPU clusters, terabytes of experience, and billion-parameter models. I built a system that proves the opposite. The core innovation of a production-grade, B2B lead generation web crawler isn't its performance, but its location: it runs entirely on an Apple M1 MacBook, with zero cloud dependencies. Its ability to navigate the sparse-reward desert of the web emerges not from brute force, but from a meticulously orchestrated multi-timescale novelty engine. This architecture, where intrinsic curiosity, predictive uncertainty, and a self-adjusting curriculum interlock, provides a general blueprint for building autonomous agents that must find needles in the world's largest haystacks.