auto-mdp-solver — the harness
The Python toolchain behind the auto-mdp-solver Claude Code plugin: a pipeline that turns a verbal dynamic-decision problem into a trained, deployable RL policy. This package is the executable half — the IR schema and interpreter, the gates, and the tuning driver. The plugin (skills, spec, examples) ships separately and installs this package into the workspace venv on first run.
| package | what it is | entry point |
|---|---|---|
mdp_ir |
MDP-IR schema (pydantic), reference interpreter, catalog ⊕ selection layering, execution laws, bit-exact differential runner | python -m mdp_ir <schema>, python -m mdp_ir.laws <dir>, python -m mdp_ir.differential <schema> |
mdp_conformance |
spec-conformance checks on a generated domain | python -m mdp_conformance <dir> |
mdp_gates |
eval-gate comparison of a candidate against baselines under the shared seed protocol | python -m mdp_gates |
mdp_tuning |
Optuna tuning driver over a domain's training script | python -m mdp_tuning <dir> ... |
mdp_stage |
the pipeline's entry gates — where a domain folder stands, and whether an op may start | python -m mdp_stage <dir> [--for <op>] |
Install
pip install "auto-mdp-solver[domain]" # everything a generated domain needs (SB3 + torch + pandas + plotly + pytest)
pip install auto-mdp-solver # harness only, torch-free: IR, conformance, gates, tuning
pip install "auto-mdp-solver[dev]" # harness + pytest
# the same, straight from the repo at a release tag (no PyPI needed):
pip install "auto-mdp-solver[domain] @ git+https://github.com/tong-wang/auto-mdp-solver@v0.10.17#subdirectory=harness"
Requires Python ≥ 3.12. The version number tracks the plugin release it was
cut from (v0.10.17 ↔ plugin 0.10.17), so the skills and the harness a
workspace holds are comparable by one number.
Documentation, the per-domain spec, the frozen example domains and the test cases live in the repository. MIT license.
1 maintainer
Metadata
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Release files for auto-mdp-solver 0.11.11
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Release files / auto_mdp_solver-0.11.11.tar.gz
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