arXiv cs.AI· Md Mirajul Islam·· 13 小时前AI 评分26
面向人本环境的离线学徒学习：Md Mirajul Islam 博士论文
Offline Apprenticeship Learning for Human-Centric Environments
AI 导读
Md Mirajul Islam 的博士论文提出一系列离线学徒学习（AL）框架，围绕奖励建模与示范质量两大挑战展开。奖励建模方面，EM-EDM 按潜在奖励函数聚类示范并推导簇专属策略，THEMES 借助分层学习的奖励调节器将轨迹切分为时间连贯的子轨迹，在一个教育数据集和三个临床数据集上优于扁平与分层 AL 基线。
正文 · 原文
Abstract:Effective decision-making in human-centric environments such as Intelligent Tutoring Systems (ITSs) and clinical healthcare requires policies that are adaptive, data-efficient, and ethically deployable. Apprenticeship Learning (AL) infers policies directly from expert demonstrations by recovering the underlying reward structure, enabling strictly offline learning without environment interaction. This dissertation develops a progression of offline AL frameworks organized around two challenges: reward modeling and demonstration quality. For reward modeling, EM-EDM clusters demonstrations by latent reward functions and induces cluster-specific policies, and THEMES partitions trajectories into temporally coherent sub-trajectories guided by a hierarchically learned reward regulator; across one educational and three clinical datasets, THEMES outperforms flat and hierarchical AL baselines. For demonstration quality, HALIDE integrates continuous quality signals into hierarchical policy induction, enabling principled reuse of mixed-quality data under distribution shift. CCHAL further attributes quality to individual decisions via Bayesian credit assignment, dual-arm criticality scoring that contrasts expert and non-expert behavior, and a Bradley-Terry contrastive IRL objective over failure trajectories, outperforming baselines including HALIDE on three clinical EHR datasets and an ITS dataset. Finally, a real-world classroom evaluation of StuEDM, an EDM-induced policy that preserves student agency and intervenes only on disagreement, shows significant learning gains over an Expert baseline and a higher pretest-adjusted posttest score than a CQL policy trained on a far larger corpus, while overriding students less than half as often. Together, this work establishes offline AL as a practical, data-efficient, and ethically viable paradigm for human-centric environments.
| Comments: | Md Mirajul Islam's PhD Dissertation (this https URL) |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.28716 [cs.LG] |
| (or arXiv:2610.28716v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.28716 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Md Mirajul Islam [view email]
[v1]
Tue, 20 Oct 2026 17:56:15 UTC (3,147 KB)
来源：arXiv cs.AI · arxiv.org