State Space Disaggregation
Python code for progressive state-space disaggregation in infinite-horizon dynamic programming.
State abstraction
State-space disaggregation
Publication research code
Progressive refinement of aggregated state spaces for infinite-horizon dynamic programming, accompanying the ICAPS 2024 paper.
- Problem
- Large MDPs make direct dynamic programming expensive; useful state distinctions must be recovered from coarse abstractions.
- Approach
- Aggregate Bellman updates alternate with partition refinement based on value or policy information.
- Implemented
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- Models include Rooms, Garnet, racetracks, tandem queues, and weakly coupled MDPs.
- An experiment runner compares solvers and exports runtime measurements to spreadsheets.
- Engineering
- Separate model, solver, and partition utilities with a documented experiment entry point. This historical experiment repository has no automated test suite or CI workflow in the audited public tree.
Scope. Publication-specific research code; the full experiment campaign is documented as taking several days.