A game. An experiment. A small Rust library.
Give them a habit to notice. Change how you move. See which tactics they try next.
Their tactics adapt.
The combat rules stay fixed.
01 / Play it
Two waves. Three enemy roles. Watch their choices in the notebook beside the field.
Keyboard + mouse to play. On touch, choose Watch demo. No account or model download.
Try the guided playtest →The game uses the original reward rule. Learning changes tactic odds after observed outcomes; later missions are not guaranteed to be harder. This live session is an example, not a controlled benchmark.
02 / The boundary
Keep your movement, perception, combat and abilities. Tactical Air selects among the actions you allow, then learns from the result you report.
Try a tiny, independent host →Your host supplies the situation and legal tactics.
The library samples a tactic from learned preferences.
Your game executes it and measures what happened.
Bounded feedback adjusts the next choice. Save or inspect memory.
03 / The evidence
The results include both. These experiments measure enemy damage against scripted movement—not whether humans have more fun.
Mean damage in four-round windows after 24 training rounds. The frozen branch starts from the identical trained memory. Intervals below resample 32 paired policy-seed means; secondary comparisons are exploratory.
The original learner recovered after a circling reversal, gaining 18.70 HP over frozen memory. It still trailed the selected fixed strategy. Against feints, continuing to learn lost 4.73 HP.
Original experiment · 8,568 encounters →A 50/50 team blend weakened performance. Keeping full personal credit with a teammate bonus preserved reversal learning, but failed to fix the original feint regression. Neither candidate is in the game.
Additive test · 5,376 candidate encounters →Three new cover/spawn layouts and 16 fresh seeds gave different learning effects. The teammate bonus added just 0.03 HP [−0.13, 0.19] on feints, averaged across those worlds.
Fresh scenarios · 11,760 encounters →Late-round enemy damage. Learning benefit is continued minus frozen memory. Each world has 16 seeds; the worlds are authored cases, not a random map sample.
04 / Make it yours
Rust at the core. WebAssembly in the browser. MIT-licensed source, versioned downloads, and an independent example to start from.
Extract, serve the folder over HTTP, and run the guard example. It includes the compiled library and typed client.
python3 -m http.server 8080Or run the Rust host from the source bundle:
cargo run --locked --release \
-p tactical-air-core --example guard_hostLoading the reproducible timing report…
One guard, two actions, one context; choose + feedback + commit. Median batch means after warm-up, excluding combat and rendering. One Mac is not a capacity or mobile-performance guarantee.
Timing data and environment →Verified here: Rust and native C on macOS arm64; Wasm in Node and desktop Chromium. Other platforms need validation. Unity, Godot and Unreal adapters remain deferred. Downloads are experimental; no npm or crates.io registry release is claimed.
What comes next
The next evidence comes from people: can they describe an enemy’s learned tendency, exploit it, and still find the fight fair? The study records those answers before revealing the conditions.
Take the field playtest ↗Read the protocolAbout 10–15 minutes. Keyboard + mouse. Session data stays in your browser unless you export and choose to share it. No automatic uploads.