reinforcement learning

Reinforcement learning-guided archival question answering with adaptive curriculum learning

A reinforcement learning-guided question answering model specifically designed for archival question answering scenarios, incorporating a hierarchical reward mechanism that evaluates answer quality across multiple dimensions including factual accuracy, semantic relevance, and provenance clarity is proposed.

Executable flexibility coordination of electric vehicle aggregators via temporal reinforcement learning in coupled electricity markets

Deep reinforcement learning enhanced coordination of hybrid battery–supercapacitor storage under renewable variability in DC microgrids

Decentralized Topology Robustness Optimization for IoT via Multi-Agent Graph Reinforcement Learning

The robustness of Internet of Things (IoT) communication topologies against internal failures and external perturbations is a fundamental prerequisite for maintaining system stability. This paper studies IoT topology robustness from two perspectives: robustness metric and optimization. Existing robustness metrics, prim...

Reinforcement learning-based distributed adaptive consensus control for stochastic non-affine multi-agent systems subjected to switching topologies and hybrid uncertainties

Spatiotemporal graph-based surrogate modeling and deep reinforcement learning for multi-layer injection-production decision in waterflood reservoirs

Reinforcement learning-based adaptive optimal finite-time control for stochastic nonlinear systems with output hysteresis using identifier-critic-actor framework

Dynamic collaborative search for multiple autonomous underwater vehicles based on hierarchical multi-agent reinforcement learning

A reinforcement-learning-guided memetic Narwhal Optimization Algorithm for global and engineering optimization

The Narwhal Optimization Algorithm is a recent swarm metaheuristic that, like most population-based optimisers, is prone to premature convergence, is sensitive to random initialisation, and relies on a rigid, schedule-driven exploration–exploitation balance. This paper develops and rigorously evaluates two enhanced v...

NaviRAG: Learning to Navigate Knowledge Graphs for Retrieval-Augmented Generation

Retrieval-Augmented Generation (RAG) has become a fundamental paradigm for enhancing Large Language Models (LLMs) with external knowledge. However, while recent structure-augmented approaches organize documents into graphs to improve information access, their retrieval strategies remain largely static, relying on simil...

Quantum Machine Learning for Adaptive Reinforcement Learning

Quantum Machine Learning (QML) offers a paradigm shift in computational power, potentially enabling breakthroughs across diverse fields. This paper investigates the application of QML to reinforcement learning, specifically focusing on designing a novel framework that leverages dynamic quantum fields to enhance learnin...

Quantum Error Correction Code Dynamic Optimization and Adaptive Adjustment

This paper presents a novel approach to dynamic optimization and adaptive adjustment of quantum error correction (QEC) codes. Traditional QEC codes often rely on fixed parameters, which can be suboptimal in the presence of fluctuating noise environments. We introduce a framework that leverages the redundancy inherent i...