reinforcement learning

Mapping the RAG landscape: a four-axis taxonomy of efficiency, defense, interactivity, and reasoning

Large Language Models (LLMs) have demonstrated remarkable fluency across many tasks but remain limited by their static, parameter-bound knowledge and their susceptibility to hallucinating information. Retrieval-Augmented Generation (RAG) addresses these issues by incorporating external retrieval into the generation pro...

Simulated Morality, Misplaced Trust: The Risks of Treating AI as a Moral Partner

The article proposes a reframing of alignment from an ethical-pedagogical project to an institutional one: instead of presuming or attempting to cultivate artificial moral agents on the basis of behavioral proxies, governance should focus on designing legal, technical, and organizational structures that constrain and r...

Answer Is Cheap, Show Me the Evidence! Augmenting Automated Vulnerability Assessment with Evidence

This work proposes EAVA, a novel framework that effectively leverages LLMs to perform SV assessment and provide supporting evidence and enables large-scale reasoning trajectory annotation using off-the-shelf LLMs and adopts a two-stage training paradigm.

An integrated framework based on reinforcement learning for performance-driven residential block generation

Framework for Bridge Maintenance Decision-Making Based on Improved Deep Q-Networks

This study proposes a deep reinforcement learning framework for bridge maintenance decision-making and indicates that Prioritized experience replay (PER) Deep Q-Networks (DQN) fits engineering data samples well and shows stronger cost-reduction performance than Double DQN.

Physics-Guided reinforcement learning for Force-Position synergistic Anti-Slip control of hydraulic supports in steeply inclined seams

A network-aware hybrid deep reinforcement learning optimization framework for microgrid energy management under uncertainty

Neuro-fault recognition-based cybersecurity framework for cardio-hydro-electromechanical stimulators

The modern bio-medical systems are equipped with communication networks for remote control and monitoring purposes. However, the networked systems are susceptible to cyber-attacks, which degrade system operation. This work addresses the cybersecurity problem of cardio-hydro-electromechanical stimulator (C-HEMS) under m...

Large Organismic Model: A Developmental Architecture for Embodied, Affective, and Self-Directed Artificial Intelligence

We propose the Large Organismic Model (LOM), a developmental architecture for artificial intelligence. Rather than reporting the performance of an implemented system, this position paper formulates a testable computational hypothesis that integrates ideas from biology, developmental neuroscience, reinforcement learning...

Research-Group-Ai/MEC_IIoT_E_DDPG_LS_MCOR_TD3_Code: MEC IIoT E-DDPG-LS and MCOR-TD3 Code v1.0.0

MCOR-TD3 for MEC-Enabled IIoT — v1.0.1 This release contains Python code associated with the paper "Adaptive Task Caching and Resource Allocation in MEC-Enabled IIoT Using Deep Reinforcement Learning." It supports simulation of cooperative task offloading and computing-resource allocation using MCOR-TD3 to minimize max...

Improving Electric Grid Performance Through Forecasting and Reinforcement Machine Learning Model

Replication Package for "The State of RL4SE: A Task-Oriented Review of Research on Reinforcement Learning for Software Engineering"

This record contains the replication package for the survey “The State of RL4SE: A Task-Oriented Review of Research on Reinforcement Learning for Software Engineering.” The package supports the study-identification, screening, classification, and artifact-availability results reported in the survey. It contains: • cand...