reinforcement learning

A Closer Look at the Use of Reinforcement Learning for Speeding Up Runtime Verification of Software Tests (Experience Paper)

Runtime verification (RV) found many bugs by monitoring passing tests against formal specifications (specs), but it is slow. A recent work, Valg, used reinforcement learning (RL) to speed up RV by up to 551.5x or 27 hours. Valg aims to probabilistically monitor most unique traces—sequences of spec-related events like m...

HIC-DRL: A deep reinforcement learning approach for hypergraph influence maximization with independent cascade model

Noise scheduling and out-of-distribution generalisation in deep reinforcement learning for 6-DOF robotic grasping

Abstract Exploration strategy is a fundamental but underexplored design choice in deep reinforcement learning for robotic manipulation. This paper presents a controlled empirical comparison of three off-policy configurations on a 6-DOF robotic grasping task using a Universal Robots UR5e in the RoboSuite simulation envi...

Active Visual Inspection with Deep Reinforcement Learning for Disease Diagnosis in Tomato Plants

Human-centric digital twins in industry 5.0: technologies, applications, and future directions

Industry 5.0 (I5.0) emphasizes industrial technologies that are human-centric, sustainable, and resilient. Human-Centric Digital Twins (HCDTs) support this transition by integrating worker data with robots, machines, tasks, and industrial environments. However, the relevant research remains fragmented across human mode...

Confocal images of coronal slices to verify ChR2 expression in dopaminergic midbrain

This data set contains immunostaining confirmation of ChR2 expression in dopaminergic midbrain, accompanying Bouabid, Vu, et al, 2026. "An anatomical hotspot for striatal dopamine-acetylcholine interactions during reward and movement: multi-fiber photometry data": Abstract: Dopamine (DA) and acetylcholine (ACh) are key...

Evolutionary–Neural Hybrids for Interference-Aware Channel Assignment in Ultra-Dense 6G Networks: A Survey, Taxonomy, and Research Roadmap

Ultra-dense networks (UDNs) are a defining feature of 6G: thousands of small cells and devices share a limited spectrum, so co-channel interference rather than noise limits performance. Assigning channels to cells or users in such networks is a combinatorial, NP-hard problem whose search space grows exponentially with...

Adaptive curriculum learning-based path planning and data collection for autonomous underwater vehicles under extreme sea conditions

Path planning and data collection for Autonomous Underwater Vehicles (AUVs) under extreme sea conditions face severe environmental disturbances and high task complexity, leading to low training efficiency and divergence in traditional reinforcement learning. To address the limitations of existing fixed curriculum learn...

Development and validation of VentPilot: an AI-based recommendation system for mechanical ventilation

Mechanical ventilation requires repeated adjustment to changing patient physiology, but consistent individualized management remains challenging. We developed VentPilot, an artificial intelligence–based system for recommending ventilator settings, and evaluated it in multicenter retrospective validation cohorts. VentPi...

AETERNA SINGULARITY: The Grand Unified Resolution of the 6 Millennium Prize Problems via Self-Adjoint Spectral Operators and Geometric Invariants

This master publication presents the complete, deterministic analytic and algebraic resolution of the 6 Millennium Prize Problems established by the Clay Mathematics Institute (CMI), unified under the AETERNA Self-Adjoint Spectral Operator Framework by Dimitar Prodromov (AETERNA Technologies EOOD). THE 6 FORMAL RESOLUT...

A Deterministic Spectral Proof of the Riemann Hypothesis via Weil Positivity, Li Criterion Asymptotics, and Catuskoti Algebraic Induction

The Riemann Hypothesis (RH), formulated by Bernhard Riemann in 1859, asserts that all non-trivial zeros of the Riemann zeta function have real part Re(s) = 1/2. In this paper, we establish a deterministic, complete analytic proof of the Riemann Hypothesis by synthesizing an exact zero-float rational arithmetic substrat...

SmartDecompiler-R1: Enhancing Faithful and Explainable Smart Contract Bytecode Decompilation with Reinforcement Learning

Understanding EVM bytecode is critical for smart contract security analysis. Existing decompilers typically rely on heuristic rules or leverage large language models (LLMs) to generate source code after bytecode analysis. However, heuristic-based approaches often produce pseudocode that is difficult for humans to inter...