# UP Quark

**One-liner**  
A reinforcement learning model that anticipates reward collapse before it happens by monitoring pre-critical entropy dynamics.

## Why it matters
Most RL agents fail only *after* collapse is observable. UP is designed to detect early warning signals and act before irreversible degradation occurs.

## Core idea 
- Environment: Near-critical reward landscapes
- Signal: Entropy acceleration
- Mechanism: Pre-collapse thresholding
- Outcome: Preventive avoidance

## What makes it different
- Predictive, not reactive
- Collapse-aware optimization
- Stability-first policy modulation

## Status
Experimental research prototype.