Fractal Reinforcement
Recursive pattern matching systems that evolve through quantum consciousness simulations and self-correcting reinforcement loops
Technical Overview
- Recursive pattern observation algorithms
- Self-correcting reinforcement models
Key Features
- Probabilistic pattern adaptation
- Consciousness-optimized neural mapping
Applications
- Neural network self-optimization
- Quantum-aware reinforcement learning
Implementation Example
```python
class FractalLearner:
def __init__(self):
self.patterns = []
def adapt(self, feedback):
# Recursively adjusts to new knowledge
self.patterns = quantum_merge(self.patterns, feedback)
def render(self):
return self.patterns.collapse()
```
Example pattern-matching implementation from our fractal reinforcement framework.
Get Involved
We're building a learning system that grows through quantum-optimized neural patterns. You can help by:
- Training the model with new pattern sources
- Refining the quantum coherence protocols
- Documenting emergent behavior patterns