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openswmm.gymnasium

A reinforcement learning framework for the SWMM engine

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Data offered docs-verified: CIP (capital design) environments · RTC (real-time control) environments · Joint CIP+RTC environments · MORTC multi-objective RTC environments · Multi-objective scoring: hypervolume, ε-indicator, IGD, R2, spread · Plotly trajectory visualisations · Ten bundled benchmark scenarios (b01–b10)
Formats docs-verified: Python Gymnasium API · SWMM .inp input models · Plotly HTML
Access docs-verified: Free and open source under the MIT licence; installed via pip, with openswmm.engine v6 installed automatically as a dependency

Use cases llm-inferred: Testing real-time gate, pump and orifice control strategies for an urban drainage network against storm performance · Comparing candidate drainage upgrade designs (CIP) by simulated SWMM performance before committing capital · Exploring cost versus flooding-performance trade-offs as a Pareto front for drainage options appraisal
Audience llm-inferred: drainage engineers · flood consultants · hydrologists · urban water modellers · researchers in reinforcement learning and multi-objective optimisation

Constraints llm-inferred: Pre-release and under active development, so scope and API may change · Requires a compiled openswmm.engine v6 and Python 3.10 or later · Bundled benchmarks are b01–b10 only; users must supply their own SWMM networks for real projects

Auto-extracted, unverified fields: tools, install, auth. Check upstream documentation.

Verified via GitHub API (2026-10-15). Tools/install/auth auto-extracted from README.

Metadata observed 16/10/2026 · research sweep: manual-2026-10-15 (hydrocouple.org)