Jonas
96b7d42c34
Registering thousands of extracted frames one-by-one via POST /frames doesn't scale, so add `spelunkai-labeling-backend ingest-session`: it writes Frame rows directly against the database (no server needs to be running) for all frame_*.png files under a directory already placed under FRAMES_ROOT. Idempotent per (session_name, frame_index), so re-running after copying more frames only inserts the new ones. Restructured the CLI into subcommands (serve / ingest-session) while keeping `spelunkai-labeling-backend` with no arguments working exactly as before (defaults to serve), verified against a live run. 22/22 backend tests pass. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
SpelunkAI
A multi-stage AI system that learns to play Spelunky Classic HD: trained perception models, deterministic HUD-state classifiers, and a control agent that first imitates human play (behavior cloning) and later learns autonomously (reinforcement learning).
Full project spec, architecture, timing constraints, and roadmap live in
CLAUDE.md — that document is the source of truth. This README is just
a map of the repo.
Layout
| Directory | Component | Spec section |
|---|---|---|
recording/ |
Recording Tool — captures gameplay video + input log on the recording PC | 3.1 |
labeling/ |
Web-Based Labeling Tool — backend + frontend for bounding-box labeling | 3.2 |
training/ |
Per-set training pipeline for the anchor-free CNN detectors | 3.3 |
inference/ |
Runtime inference loop — capture → detectors → HUD classifiers → state vector, within the 66ms/tick budget | 3.4, 3.5 |
control/ |
Control Agent — behavior cloning now, RL later | 3.6 |
Each component directory has its own README with more detail and its own .venv
(per CLAUDE.md §6), since components run on different machines (recording PC vs.
jai) and have independent dependencies.
Status
Repo scaffold only — no components are implemented yet.