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reinforcement learning

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#reinforcement learning Open access Sep 2026

Reading Journey to the West as an ML Development Pipeline

This paper presents a continuous, stage-by-stage structural correspondence between the 100-chapter classical Chinese novel Journey to the West (Xiyouji, c. 1592) and the modern development pipeline for Large Language Models (LLMs). Without asserting that the historical author predicted modern technology, the analysis d...

コスパン · 0 citations
#reinforcement learning Open access Sep 2026

Retrospective registration of a systematic review protocol: AI-Based Intrusion Detection and Prevention: A Systematic Review and Adversarial-Validity Audit of Benchmark Datasets

Retrospective registration of a systematic review protocol. This review was conducted and submitted prior to registration; no prospective registration was performed. The review synthesizes AI-based techniques for intrusion detection and prevention, drawing on 71 peer-reviewed studies published between 2019 and 2025, se...

Hanan Moufid, Mohamed El Ghazouani, Moulay Ahmed El Kiram · 0 citations
#reinforcement learning Open access Sep 2026

Reinforcement Learning-Based Intelligent Adaptive Protection Relays: A Systematic Comparative Review of Algorithms, Applications, and Performance Metrics

The accelerating integration of inverter-based resources (IBR) and distributed generation (DG)—with IBR penetration now exceeding 40% in many distribution networks and fault-current contribution curtailed to 1.0–1.5 p.u. versus 5–10 p.u. for synchronous generators—has measurably reduced the efficacy of conventional pow...

Sipho P Lafleni, Tlotlollo Sidwell Hlalele, Mbuyu Sumbwanyambe · 0 citations
#reinforcement learning Open access Sep 2026

From pixels to actions: grounding in vision-language models

Vision-Language Models (VLMs) have demonstrated impressive capabilities across a broad range of perception tasks, yet systematic weaknesses remain concerning fine-grained spatial reasoning, precise visual localisation, or grounded action in interactive environments. This thesis investigates the architectural and traini...

Georgios Michail Pantazopoulos · 0 citations
#reinforcement learning Open access Sep 2026

UniRG-CXR: model weights and inference code

Archival snapshot of the UniRG-CXR chest X-ray report generation model (weights and inference code), exactly as used in the study 'Scaling medical imaging report generation with multimodal reinforcement learning'. Mirrors the Hugging Face repository microsoft/UniRG-CXR at git revision cd5a95fa837c92180e14ed0ddd7f079150...

Qianchu Liu, Sheng Zhang, Guanghui Qin · 0 citations

Reinforcement Learning‐Based Event‐Triggered Resilient Coordination Control for Spacecraft Formation Flying

ABSTRACT This paper studies resilient coordination control for a spacecraft formation subject to limited communication resources, admissible communication‐link faults, actuator faults, and uncertain nonlinear dynamics. A distributed data‐driven coordination scheme is developed using a resilient observer, event‐triggere...

Yue Gao, Haowen Tian, Hao Liu et al. · 0 citations
#reinforcement learning Conference Sep 2026

A machine learning-based model for evaluating and optimizing efficiency in the green circular economy

A closed-loop energy efficiency assessment and global optimization architecture for green circular economy based on spatiotemporal graph attention mechanism and constrained multi-objective deep reinforcement learning and an adaptive feature correction operator is constructed to achieve lossless decoupling of transient...

Bin Wang · 0 citations
#reinforcement learning Open access Sep 2026

Modeling ownership in non-human species: an argument for the domestic dog

The concept of ownership has been extensively examined within the context of human psychology, with a strong focus on how individuals represent relationships between people and objects (e.g., Boyer, 2023;Nancekivell et al., 2019). Comparative work has been relatively limited, with a small number of studies exploring th...

Emily Sundby, Valerie A. Kuhlmeier · 0 citations

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MIT News · Artificial Intelligence Oct 23, 2026

Q&A: How AI came to model the “messy” human mind

Assistant Professor Benjamin Lindquist studies how engineers deliberately injected randomness into early AI, giving rise to deep-learning models that sometimes hallucinate.

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