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20022026
most citedA Brief Survey of Deep Reinforcement Learning

4.4k citations

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74 papers · 1 filter

cs.LG2026

The Good, the Bad, and the Ugly of Markov Boundary for Tabular Prediction

Shu Wan, Abhinav Gorantla, Huan Liu +1

Under standard graphical assumptions, the Markov boundary of a target variable is the smallest set of features that renders every other feature redundant. Once the boundary is obse…

cs.LG2026

Counterfactual Modeling with Fine-Tuned LLMs for Health Intervention Design and Sensor Data Augmentation

Shovito Barua Soumma, Asiful Arefeen, Stephanie M. Carpenter +2

Counterfactual explanations (CFEs) provide human-centric interpretability by identifying the minimal, actionable changes required to alter a machine learning model's prediction. Th…

cs.LG2026

The Two-Stage Decision-Sampling Hypothesis: Understanding the Emergence of Self-Reflection in RL-Trained LLMs

Zibo Zhao, Yuanting Zha, Haipeng Zhang +1

Self-reflection capabilities emerge in Large Language Models after RL post-training, with multi-turn RL achieving substantial gains over SFT counterparts. Yet the mechanism of how…

cs.LG2025

Diffusion Policies with Offline and Inverse Reinforcement Learning for Promoting Physical Activity in Older Adults Using Wearable Sensors

Chang Liu, Ladda Thiamwong, Yanjie Fu +1

Utilizing offline reinforcement learning (RL) with real-world clinical data is getting increasing attention in AI for healthcare. However, implementation poses significant challeng…

cs.LG2025

MNIST-Gen: A Modular MNIST-Style Dataset Generation Using Hierarchical Semantics, Reinforcement Learning, and Category Theory

Pouya Shaeri, Arash Karimi, Ariane Middel

Neural networks are often benchmarked using standard datasets such as MNIST, FashionMNIST, or other variants of MNIST, which, while accessible, are limited to generic classes such…

cs.LG2025

Machine Learning Model Integration with Open World Temporal Logic for Process Automation

Dyuman Aditya, Colton Payne, Mario Leiva +1

Recent advances in Machine Learning (ML) have produced models that extract structured information from complex data. However, a significant challenge lies in translating these perc…