reinforcement learning

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...

Title: Algorithmic Emergence of Self-Organizing Structures

This paper explores the potential of algorithmic emergence to generate complex, fractal-like structures with inherent properties of self-replication and adaptation. We propose a novel design incorporating a genetic algorithm coupled with reinforcement learning to iteratively refine a structure, fostering self-organizat...

Multi-Agent Reinforcement Learning for Stochastic OSAT Dispatching: A Matched Architecture Benchmark

This is the author’s preprint version. It has not undergone journal peer review. The manuscript presents a matched stochastic benchmark of multi-agent reinforcement-learning architectures and heuristic dispatching policies for multi-stage OSAT manufacturing.

Probability-Based Constraint Satisfaction with Dynamic Landscape

This paper investigates the application of probability-based constraint satisfaction (CS) algorithms to dynamic problem environments. Traditional CS methods often struggle with complex landscapes and require extensive search, leading to computational bottlenecks. We propose a novel reinforcement learning (RL) framework...

A Phase-Aware Prediction-Horizon Policy for Learned-Cost CEM-MPC Lane-Change Planning

These findings support maneuver phase as an interpretable context for prediction-horizon adaptation within the evaluated architecture, while limiting the conclusions to the investigated scenario and experimental setting.

Multi-Agent Reinforcement Learning with Intrinsic Motivation for Cooperative Navigation

This paper investigates the challenges of achieving robust cooperative navigation in multi-agent reinforcement learning (MARL) scenarios, addressing the limitations of relying solely on extrinsic rewards. We propose a novel framework integrating intrinsic motivation – specifically, curiosity and surprise – into each ag...

Title: Algorithmic Fractal Dimension Modulation with Dynamic Constraint Satisfaction

This paper investigates the use of dynamic constraint satisfaction within fractal generation algorithms to enhance structure complexity and robustness. Traditional fractal algorithms often rely on fixed constraints, limiting the potential for intricate and diverse forms. We propose an algorithmic approach that utilizes...

Title: Adaptive Quantum Algorithm for Non-Linear Optimization

This paper introduces an innovative adaptive quantum algorithm designed to address the limitations of traditional optimization techniques, particularly when dealing with non-linear problems. The algorithm leverages reinforcement learning to dynamically adjust its parameters based on the input data, offering a significa...