Vikingdude81/memory-agent-demo

🧠 NeuroSort: Memory-Augmented Agent Simulation

A real-time visualization comparing two AI agent strategies for navigating obstacle-rich environments.

What Is This?

NeuroSort demonstrates a key concept in AI agent design: frustration memory.

When agents encounter obstacles that cause failures (and penalties), a naive stochastic agent keeps trying randomly—wasting time and resources. A memory-augmented agent learns from frustration and adapts its behavior, avoiding repeated mistakes.

The Two Agents

How It Works

1. 1.The Task: Both agents try to sort an array (bubble sort style)
2. 2.The Catch: Some elements are "heavy" obstacles (gray bars) with 90% failure rate
3. 3.Failure Penalty: When an agent fails, it gets stunned for several cycles (amber glow)
4. 4.Memory Advantage: The memory agent tracks frustration per element—high frustration = skip and try elsewhere

Controls

• —Obstacle Density: % of heavy elements (requires reset)
• —Stun Penalty: How long agents are frozen after failure
• —Memory Decay: How fast frustration fades (higher = longer memory)

Key Insight

Over time, the Memory Agent (fuchsia) consistently achieves:

• —✅ More successful moves
• —✅ Fewer crashes
• —✅ Smarter resource allocation

Tech

React 18 + Vite + Tailwind CSS, deployed as Docker on HuggingFace Spaces.

Part of the [Harmonic Field Consciousness](https://github.com/vfd-org/harmonic-field-consciousness) research project