Physics-Informed Causal MDPs for Sequential Constraint Repair in Engineering Simulation Pipelines
Off-policy learning in constrained MDPs with large binary state spaces faces a fundamental tension: causal identification of transition dynamics requires structural assumptions, while sample-efficient policy learning requires state-space compression. We introduce PI-CMDP, a framework for CMDPs whose constraint dependencies form a layered DAG under a Lifecycle Ordering Assumption (LOA). We propose an Identify-Compress-Estimate pipeline: (i) Identify: LOA enables backdoor identification of causal edge weights for cross-layer pairs, with formal partial-identification bounds when LOA is violated; (ii) Compress: a Markov abstraction compresses state cardinality from 2^(WL) to (W+1)^L under lay...
| Authors | Chuhan Qiao |
|---|---|
| Primary category | cs.AI |
| Published | 20 April 2026 |
| Last updated at source | 20 April 2026 |
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