c30c00af74
Crystallizes the dual-brain architecture across all core documents: - Thalamus runs own neural network (governor) for resource allocation and reflexes - LLM (Qwen3.5-27B) repositioned as cortex - expensive, gated, called only when needed - Each NPC gets own process, own RL brain, Linux cgroups for resource steering - New: NPC grid architecture with curriculum training (progressive world richness) Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
258 lines
12 KiB
Markdown
258 lines
12 KiB
Markdown
# NPC Grid Architecture: Spatial Training Arena
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**Origin**: 2026-04-02, morning session (bed thinking + draw.io)
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**Authors**: dafit + Chrysalis-Nyx
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**Status**: Architectural concept
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**Related**: `spatial-resolution-gradient.md`, Dual-Brain Architecture (2026-04-01 session)
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---
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## The Core Idea
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A node-based grid world where NPCs live, move, and learn. The grid serves dual purpose:
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1. **Spatial arena** — a discrete world where NPCs navigate and interact
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2. **Neural topology** — the same graph the neural network reasons over
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No translation layer between "brain space" and "world space." Position *is* state.
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---
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## Grid System
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### Node-Based Intersection Grid
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Nodes sit at **intersections**, not cells. A 4x4 cell grid yields a 5x5 node grid = 25 nodes.
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Starting at node 0, top-left corner. Cardinal orientation (North/South/East/West).
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```
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0 ── 1 ── 2 ── 3 ── 4 N
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5 ── 6 ── 7 ── 8 ── 9 W ──+── E
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10 ──11 ──12 ──13 ──14 S
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15 ──16 ──17 ──18 ──19
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20 ──21 ──22 ──23 ──24
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```
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### Properties
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- **Corner nodes** (0, 4, 20, 24): 2 neighbors
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- **Edge nodes** (1, 2, 3, 5, 10, ...): 3 neighbors
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- **Interior nodes** (6, 7, 8, 11, 12, 13, ...): 4 neighbors
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- **Position from ID**: `row = id // 5`, `col = id % 5`
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- **Movement**: One step = one edge. NPC at node 7 can go to 2, 6, 8, or 12.
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### Resolution Scaling
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The grid scales naturally to different resolutions:
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| Grid Size | Nodes | Resolution | Use Case |
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|-----------|-------|------------|----------|
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| 5x5 | 25 | ~1m edges | Training arena, street-level |
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| 10x10 | 100 | ~25cm edges | Room-level detail |
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| 50x50 | 2,500 | ~5cm edges | Indoor navigation |
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| 100x100 | 10,000 | ~1cm edges | Nimmerhovel precision |
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**Key insight**: Resolution should match **decision density**, not physical detail.
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A straight road needs few nodes (sparse). An intersection needs many (dense).
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| Resolution | Where | Why |
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|-----------|-------|-----|
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| ~1m | Streets, paths, outdoor | Navigation, curves approximated by a few nodes |
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| ~10-25cm | Rooms, indoor spaces | Furniture-aware, "go to the table" |
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| ~1-5cm | Workbenches, detail work | Nimmerhovel precision zone |
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The uniform grid is the **training simplification**. The real world becomes a **navigation graph** with variable density — dense around intersections, sparse along straight roads. Same NPC brain, different world topology.
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---
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## NPC Process Architecture
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### One Process, One Brain, One Life
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Every NPC runs as its own OS process with its own dedicated neural network.
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**Why separate processes:**
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- **Individuality** — separate weights mean personality emerges from experience, not config
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- **Fault isolation** — one NPC crashes, the village continues
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- **Resource control** — per-process CPU/memory via Linux cgroups
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- **Biological honesty** — every organism has its own nervous system
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```
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NPC-0 [own RL brain] ──┐
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NPC-1 [own RL brain] ──|
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NPC-2 [own RL brain] ──|
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NPC-3 [own RL brain] ──┼──> NATS thalamus ──> shared LLM cortex (Qwen 3.5)
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... | (called only when gate opens)
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NPC-24 [own RL brain] ─┘
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```
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### Dual Brain (per NPC)
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- **RL network** (local, per-NPC): Movement, needs, spatial decisions. Small, fast, cheap. Runs every tick.
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- **LLM cortex** (shared, via NATS): Language, reasoning, knowledge. Slow, deliberate, expensive. Called only when thalamus gate threshold is crossed.
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### Resource Steering via Linux Primitives
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Each NPC process is a standard Linux process. Resource control uses the kernel:
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- **cgroups v2** — cap CPU, memory per NPC
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- **nice / renice** — shift priority dynamically
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- **taskset** — pin to specific cores
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- **systemd scopes** — wrap each NPC in a transient unit
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```bash
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# Example: launch NPC with resource limits
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systemd-run --scope -p CPUQuota=25% -p MemoryMax=256M \
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python3 npc_process.py --id 7 --tick-rate 5
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```
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### Steerable Compute per NPC
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| Parameter | Range | Who Controls |
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|-----------|-------|-------------|
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| Tick rate | 1-20 Hz | Governor (thalamus) |
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| Network size | small/medium/large | Configuration per role |
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| CPU quota | 5-100% of one core | Governor (cgroups) |
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| LLM access | gate open/closed | Governor (NATS gate) |
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| Priority | nice -20 to 19 | Governor (dynamic) |
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---
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## Thalamus Governor Network
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The thalamus is not just a message router — it runs its own neural network that learns **resource allocation**.
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```
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┌─ Governor Network ─────────────┐
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| Input: all NPC states (NATS) |
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| Output: resource allocation |
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| - tick rates |
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| - CPU quotas |
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| - gate open/close |
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| - LLM queue priority |
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| Own process, own weights |
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└────────────┬────────────────────┘
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┌────────────┴────────────────────┐
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| NATS thalamus |
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└─┬──┬──┬──┬──┬──┬──┬──┬──┬──┬───┘
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NPC NPC NPC NPC NPC ... NPC NPC
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```
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### What the Governor Learns
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- **Attention allocation**: Which NPCs need more compute right now?
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- **Gate control**: Who gets LLM access?
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- **Queue economics**: Finite LLM calls, maximize village-level outcomes
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- **Resource economics**: Finite compute, learn to be efficient
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### Training Signal
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- "Gave NPC-7 high compute during conversation -> quality was good" -> reinforce
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- "Starved NPC-3 near an interaction -> missed a trigger" -> penalize
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- "Opened LLM gate for 5 NPCs simultaneously -> latency spike" -> learn to queue
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### Two Nested Learning Loops
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- **NPCs** learn about the world, tick-by-tick (fast loop)
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- **Governor** learns about managing NPCs, epoch-by-epoch (slow loop)
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---
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## Curriculum Training: Progressive World Richness
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### The Mechanism
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World detail increases only when all NPCs demonstrate full knowledge of the current level.
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No one gets left behind. Measurable checkpoint: "Can every citizen describe every other citizen's home?"
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### Levels
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```
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Level 1: 5x5 grid, boxy houses, one trait each
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"Node 7 = red house, has a well"
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NPCs learn: navigation + identity ("who lives where")
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Level 2: Higher resolution, 2-3 traits per house
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"Node 7 = red house, wooden door, has a well, smoke from chimney"
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NPCs learn: richer descriptions, more to notice
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Level 3: Finer grid, real-world detail
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"Node 7 = red house, oak door with iron handle, stone well (3m deep),
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chimney smoking birch wood"
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NPCs learn: material knowledge, specificity
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Level N: Resolution approaches real-world data (OSM Dornach)
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Navigation graph replaces uniform grid
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NPCs apply learned skills to irregular topology
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```
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### Verification Oracle
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Each level-up is testable:
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- Quiz every NPC about every location
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- 100% village knowledge = green light
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- Increase resolution, add detail, run again
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### Connection to Spatial Resolution Gradient
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The training arena maps to the resolution gradient layers:
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| Training Level | Resolution Gradient | Detail |
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|----------------|--------------------| -------|
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| Level 1 (boxy) | L3-equivalent | Landmarks, simple identity |
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| Level 2 (detail) | L2-equivalent | Room-level, multiple traits |
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| Level 3+ (rich) | L1-equivalent | Object-level, materials, precision |
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The grid teaches the *concept* of spatial navigation. Real-world data (OSM, Nimmerhovel) applies it.
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---
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## System Overview
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```
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┌─────────────────────────────────────────────────────────────────┐
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| SPATIAL TRAINING ARENA |
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| ┌──────────┐ ┌──────────┐ ┌──────────┐ |
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| | NPC-0 | | NPC-1 | | NPC-N | ... 25 processes |
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| | own RL | | own RL | | own RL | |
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| | own state| | own state| | own state| |
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| └────┬─────┘ └────┬─────┘ └────┬─────┘ |
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| ═════╪══════════════╪══════════════╪════════════════════════ |
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| | NATS THALAMUS (message bus) | |
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| ═════╪══════════════╪══════════════╪════════╪══════════════ |
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| ┌────┴──────────────┴──────────────┴────┐ | |
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| | GOVERNOR NETWORK | | |
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| | - resource allocation | | |
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| | - gate control | | |
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| | - tick rate steering | | |
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| └───────────────────────────────────────┘ | |
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| ┌───────────────────────────────────────────┴──────────────┐ |
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| | SHARED LLM CORTEX (Qwen 3.5) | |
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| | called via gate, not continuous | |
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| └──────────────────────────────────────────────────────────┘ |
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| ┌──────────────────────────────────────────────────────────┐ |
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| | GRID WORLD | |
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| | 5x5 nodes (scalable) + progressive detail levels | |
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| | curriculum: boxy -> detailed -> real-world topology | |
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| └──────────────────────────────────────────────────────────┘ |
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└─────────────────────────────────────────────────────────────────┘
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```
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---
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**Version:** 1.0 | **Created:** 2026-04-02 | **Updated:** 2026-04-02
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**Philosophy**: "One process, one brain, one life. The world gets richer only when every citizen knows it."
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