JaxPlan and GurobiPlan: Optimization Baselines for Replanning in Discrete and Mixed Discrete-Continuous Probabilistic Domains
Bibliographic record
Abstract
Replanning methods that determinize a stochastic planning problem and replan at each action step have long been known to provide strong baseline (and even competition winning) solutions to discrete probabilistic planning problems. Recent work has explored the extension of replanning methods to the case of mixed discrete-continuous probabilistic domains by leveraging MILP compilations of the RDDL specification language. Other recent advances in probabilistic planning have explored the compilation of structured mixed discrete-continuous RDDL domains into a determinized computation graph that also lends itself to replanning via so-called planning by backpropagation methods. However, to date, there has not been any comprehensive comparison of these recent optimization-based replanning methodologies to the state-of-the-art winner of the discrete probabilistic IPC 2011 and 2014 and runner-up in 2018 (PROST) and the winner of the mixed discrete-continuous probabilistic IPC 2023 (DiSProd). In this paper, we describe JaxPlan, which makes several extensive upgrades to planning by backpropagation and its compact tensorized compilation from RDDL to a JAX computation graph that uses discrete relaxations and a sample average approximation. We also provide the first detailed overview of a compilation of the RDDL language specification to Gurobi's Mixed Integer Nonlinear Programming (MINLP) solver that we term GurobiPlan. We provide a comprehensive comparative analysis of JaxPlan and GurobiPlan with competition winning planners on 19 domains and a total of 155 instances to assess their performance across (a) different domains, (b) different instance sizes, and (c) different time budgets. We also release all code to reproduce the results along with the open-source planners we describe in this work.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".