Drone Swarms Learning Melee and Ranged Battle Tactics via Self-Play

I wanted to see how far you can get with zero neural training — no gradients, no weights, no backprop. Just closed-form neuro-symbolic policies, discovered purely through self-play in a red-queen arms race, running GPU-batched so thousands of candidate strategies fight in parallel.

What genuinely surprised me is watching real tactics emerge — none of this was programmed:

⚔️ Combined arms. The fleets are mixed — fast melee kamikazes and standoff ranged units — and the swarms learn to screen their ranged shooters behind a melee wall, exactly the doctrine you’d hope for and never coded.

🎯 Focus fire & target priority. Instead of spreading damage, drones converge on the weakest/nearest enemy first, collapsing the opposing force faster — emergent kill-priority logic.

🌀 Encirclement & flanking. You can see swarms peel off to wrap around the enemy’s flanks rather than meeting head-on, denying escape and cutting angles.

🪃 Kiting. Ranged units learn to stay just outside melee reach, backpedaling while firing — the classic hit-and-run that only makes sense once you understand your own weapon range.

🐟 Cohesion vs. dispersal, dynamically. The swarm tightens into a blob for concentrated firepower, then scatters when clustering becomes a liability — a living tension between mass and spread.

And because it’s all symbolic + closed-form, every one of these behaviors is fully interpretable — I can point at the exact features driving each decision. No black box.

The most fun part: these strategies weren’t designed, debated, or trained. They were evolved — the arms race just kept escalating until the swarms got clever.