19 lines
1.0 KiB
Markdown
19 lines
1.0 KiB
Markdown
# Mission
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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.
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## Why
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- Classical aiming (analytical lead formulas) can't handle unpredictable movement
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- SNNs offer energy-efficient, event-driven learning suited to real-time control
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- Binary operations (Hamming distance, popcount) are fast and hardware-friendly
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- The long-term goal is a bot that improves through experience, not programming
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## Current State
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Two implementations exist in `SNNBot_garage/src/`:
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- **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.
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- **SNN** (`SNNBot.nim`, USE_RESERVOIR=false): LIF spiking network with SuperSpike-inspired learning. Dormant — not currently active.
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The grid hit its ceiling. The SNN path is the intended future.
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