# Mission

Build a spiking neural network (SNN) bot for Tank Royale that learns to aim at moving targets using binary operations and biologically-inspired learning rules — not analytical formulas. The system must generalize from predictable movers to evasive opponents.

## Why

- Classical aiming (analytical lead formulas) can't handle unpredictable movement
- SNNs offer energy-efficient, event-driven learning suited to real-time control
- Binary operations (Hamming distance, popcount) are fast and hardware-friendly
- The long-term goal is a bot that improves through experience, not programming

## Current State

Two implementations exist in `SNNBot_garage/src/`:
- **Grid accumulator** (`reservoir.nim`, USE_RESERVOIR=true): Lookup table mapping (vPerp, distance) → lead offset. Works (~48% hit rate) but can't scale to more inputs.
- **SNN** (`SNNBot.nim`, USE_RESERVOIR=false): LIF spiking network with SuperSpike-inspired learning. Dormant — not currently active.

The grid hit its ceiling. The SNN path is the intended future.