robert
702647d84e
TBD: parameters outside the controller, parameters contained on the old parameter doc
66 lines
2.8 KiB
Markdown
66 lines
2.8 KiB
Markdown
# AICONTROL — the AI & Control cluster's folder
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Everything our cluster writes lives here: the placeholder environment, the forecasting
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harness, the training rig, the evaluation pipeline, the safety layer and the dashboard. The
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plan we work from is [`docs/01-project/ai-control-cluster-plan-2026-2027.md`](../docs/01-project/ai-control-cluster-plan-2026-2027.md).
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## Clone and run
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The whole team shares one Python environment, pinned at the repository root. You need Python
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3.12 and [uv](https://docs.astral.sh/uv/) (one-off: `pip install uv`).
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```bash
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git clone https://git.teamshiftenergy.com/pepe/ALLSHIFT.git
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cd ALLSHIFT
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uv sync # first time: creates .venv/ with everyone's tools, a few minutes
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uv run pytest # runs our tests; all green means your setup works
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```
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`uv sync` reads `uv.lock`, so everyone gets exactly the same versions. Never commit `.venv/`.
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If you add a package, add it to the root `pyproject.toml`, run `uv lock`, and commit the
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updated `uv.lock` with your change.
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## What is where
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```
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AICONTROL/
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├── aicontrol/ ← Python package (import aicontrol)
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│ └── env/
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│ └── spaces.py ← what the agent sees and controls, built from configs/env.yaml
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├── configs/
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│ └── env.yaml ← plant sizes, observation ranges, forecast layout, PLACEHOLDER reward weights
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├── docs/
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│ └── interface-draft.md← the Simulations → AI handover, drafted for the November session
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├── tests/ ← pytest; run from the repository root with `uv run pytest`
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└── README.md
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```
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Folders that will appear as the work does: `env/placeholder.py` (the placeholder
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environment), `forecast/`, `train/`, `evaluate/`, `safety/`, `dashboard/`.
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## Who does what
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| Person | Seats |
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|---|---|
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| Lead | RL Environment + Safety |
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| Person 2 | RL Training |
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| Person 3 | RL Evaluation + Explainability & Dashboard |
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| Person 4 | Forecasting |
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## Q1 goal — what runs on Friday 16 October
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1. The placeholder environment, with a determinism test and an energy test.
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2. A training script (PPO or SAC on the placeholder, three seeds, logged to Weights & Biases).
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3. The results pipeline: any controller × any scenario → one row; the comparison table; the
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column list for Business. Controller names reserved: `rule_based`, `mpc`,
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`perfect_knowledge`, `agent`.
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4. The forecasting harness with "same as yesterday" baselines and a skill score.
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5. The interface document, ready for the joint session with Simulations.
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## Rules we keep
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- Never commit `.venv/`, Weights & Biases run folders, or raw data downloads (the `.gitignore`
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covers them).
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- Machine-made tables are Parquet; hand-written settings are YAML; documents are Markdown.
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- The placeholder environment never gets better physics. The day the twin runs, we point at it
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and delete the placeholder.
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