Jonas
65155ce22e
Add top-level README, .gitignore, and per-component directories (recording, labeling backend/frontend, training, inference, control) with READMEs and pyproject.toml/package skeletons per CLAUDE.md §2-3. No implementation yet, just structure to build against. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
28 lines
1.3 KiB
Markdown
28 lines
1.3 KiB
Markdown
# SpelunkAI
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A multi-stage AI system that learns to play *Spelunky Classic HD*: trained perception
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models, deterministic HUD-state classifiers, and a control agent that first imitates
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human play (behavior cloning) and later learns autonomously (reinforcement learning).
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Full project spec, architecture, timing constraints, and roadmap live in
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[`CLAUDE.md`](./CLAUDE.md) — that document is the source of truth. This README is just
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a map of the repo.
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## Layout
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| Directory | Component | Spec section |
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| [`recording/`](./recording) | Recording Tool — captures gameplay video + input log on the recording PC | 3.1 |
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| [`labeling/`](./labeling) | Web-Based Labeling Tool — backend + frontend for bounding-box labeling | 3.2 |
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| [`training/`](./training) | Per-set training pipeline for the anchor-free CNN detectors | 3.3 |
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| [`inference/`](./inference) | Runtime inference loop — capture → detectors → HUD classifiers → state vector, within the 66ms/tick budget | 3.4, 3.5 |
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| [`control/`](./control) | Control Agent — behavior cloning now, RL later | 3.6 |
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Each component directory has its own README with more detail and its own `.venv`
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(per `CLAUDE.md` §6), since components run on different machines (recording PC vs.
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`jai`) and have independent dependencies.
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## Status
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Repo scaffold only — no components are implemented yet.
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