aiqbench analysis
This research introduces Maturing Markov Decision Processes (MMDPs), a framework that formalizes the trade-off between committing to actions and deferring decisions as new information arrives. By establishing the Expiring-Actions-First Principle, the study provides theoretical conditions for leveraging forecast refinement to improve learning efficiency in sequential decision-making tasks. The findings demonstrate that incorporating commitment timing as an inductive bias yields measurable performance gains in both synthetic environments and production-scale cash management scenarios.
When to Commit and When to Defer: Maturing Markov Decision Processes under Refining Information and Expiring Opportunities
arXiv:2606.18820v2 Announce Type: replace Abstract: Sequential decisions often become better informed as action opportunities expire. We introduce Maturing Markov Decision Processes (MMDPs), a structured subclass of augmented finite-horizon Markov decision processes, for organizing when to commit and when to defer. The Expiring-Actions-First Principle provides a sufficient condition for deferral based on information gain and delay loss. This structure motivates stage-local policies, action abstraction, and policy-guided planning. Replenishment experiments show improved learning and test performance under the MMDP interface and, in the primary setting, a larger test-performance gain from forecast refinement. In synthetic cash management, MMDP improves PPO test returns across both network sizes; five-account equal-size mask controls show that action count alone does not explain its learned gain, and the advantage persists with action abstraction and policy-guided search. A production-scale cash-management case study adds complementary evidence. Together, these results support commitment timing as a useful inductive bias for learning and planning.
Abstract:Sequential decisions often become better informed as action opportunities expire. We introduce Maturing Markov Decision Processes (MMDPs), a structured subclass of augmented finite-horizon Markov decision processes, for organizing when to commit and when to defer. The Expiring-Actions-First Principle provides a sufficient condition for deferral based on information gain and delay loss. This structure motivates stage-local policies, action abstraction, and policy-guided planning. Replenishment experiments show improved learning and test performance under the MMDP interface and, in the primary setting, a larger test-performance gain from forecast refinement. In synthetic cash management, MMDP improves PPO test returns across both network sizes; five-account equal-size mask controls show that action count alone does not explain its learned gain, and the advantage persists with action abstraction and policy-guided search. A production-scale cash-management case study adds complementary evidence. Together, these results support commitment timing as a useful inductive bias for learning and planning.
37 pages, 5 figures. Substantially revised formulation, theoretical analysis, and experiments; expanded appendices. Research code: this https URL (v1.0.0)
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2606.18820 [cs.LG]
(or arXiv:2606.18820v2 [cs.LG] for this version)
Full text under the publisher's open licence · 179 words
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