New Taxonomy Guides Imitation Learning Experts for Stochastic Optimization

Global: New Taxonomy Guides Imitation Learning Experts for Stochastic Optimization

Researchers introduced a systematic taxonomy of expert models for imitation learning (IL) applied to large‑scale combinatorial optimization problems that are formulated as sequential decision problems under uncertainty. The same work proposes a generalized Dataset Aggregation (DAgger) algorithm capable of handling multiple expert queries, expert aggregation, and varied interaction strategies, and evaluates the approach on a dynamic physician‑to‑patient assignment problem with stochastic arrivals and capacity constraints.

Taxonomy of Expert Models

According to the authors, experts are classified along three dimensions: (i) treatment of uncertainty—ranging from myopic and deterministic formulations to full‑information, two‑stage stochastic, and multi‑stage stochastic models; (ii) level of optimality—distinguishing task‑optimal experts from approximate ones; and (iii) interaction mode with the learner—spanning one‑shot supervision to iterative, interactive schemes. This framework is intended to unify previously disparate expert constructions.

Generalized DAgger Framework

The paper outlines a generalized DAgger algorithm that extends the classic imitation‑learning loop. It permits the learner to query multiple experts, combine their guidance, and adapt the frequency and timing of interactions based on the chosen expert taxonomy, thereby offering greater flexibility than traditional single‑expert setups.

Experimental Evaluation

In the reported experiments, the authors applied the framework to a dynamic physician‑to‑patient assignment scenario characterized by stochastic patient arrivals and limited capacity. They compared learning outcomes across the defined expert types and interaction regimes, measuring solution quality and the number of expert demonstrations required.

Key Findings

The results indicate that policies learned from stochastic experts consistently outperform those derived from deterministic or full‑information experts. Moreover, interactive learning regimes improve solution quality while requiring fewer expert demonstrations, highlighting the efficiency gains of iterative supervision.

Practical Implications

When stochastic optimization becomes computationally intensive, the authors note that aggregated deterministic experts can serve as an effective alternative, delivering comparable performance with reduced computational overhead.

Broader Impact

By providing a unified taxonomy and a flexible learning algorithm, the study offers a structured approach for future research on imitation learning in uncertain combinatorial settings, potentially guiding the design of more data‑efficient and robust decision‑making systems.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.