reinforcement learning

Dynamic Resource Allocation via Relational Reinforcement Learning

This paper introduces a novel Relational Reinforcement Learning (RRL) framework designed to address the challenges of dynamic resource allocation in complex environments. Traditional Reinforcement Learning (RL) approaches often fall short when dealing with environments that constantly evolve and demand adaptive resourc...

基于量子机器学习的动态拓扑优化算法

This paper introduces a novel dynamic topology optimization algorithm powered by quantum machine learning. Traditional topology optimization methods often require manual design of the topology, leading to limited flexibility and computational expense. Our algorithm leverages quantum machine learning to automatically le...

Title: Adaptive Reinforcement Learning Reward Adjustment

This paper investigates a novel reinforcement learning algorithm utilizing adaptive reward function adjustment to enhance learning performance. Traditional reinforcement learning often relies on fixed reward functions, which can struggle to adapt to complex environments and unexpected situations. We propose a method th...

Industrial waste valorization for sustainable self-healing epoxy vitrimer hybrid composites: Taguchi optimization and machine learning-based performance prediction

This study investigates the development of sustainable self-healing vitrimer composites reinforced with recycled carbon fibers (rCF), waste cotton textile fibers (WCTF), and graphene nanoplatelets (GNP). The rCF, WCTF and GNP were in the range of 20–30%, 10–20%, and 0.5–1%, respectively, and an orthogonal design (L9) w...

Title: Dynamically Generated Geometric Patterns for Computational Geometry

This research investigates the application of reinforcement learning to automatically generate and optimize geometric patterns, focusing on identifying novel patterns exhibiting complex, self-organizing behavior. Traditional geometric pattern generation often relies on predefined rules, limiting the potential for truly...

Progressive Dribbling Learning Model to Improve Dribbling Skills in Elementary School Children Aged 9–12 Years at SD IT Mutiara

This research aims to develop a feasible, practical, and effective progressive dribbling learning model to improve dribbling skills in elementary school children aged 9–12 years. The research was motivated by the low dribbling skills of students due to learning models that are still conventional, lack variety, and do n...

The structure of infrastructure adoption: competition of hydrogen and electric for long-haul freight decarbonization

Long-haul freight decarbonization requires deploying hydrogen refueling stations (HRSs) across networks designed around incumbent diesel supply. Existing planning tools treat node-level demand as uniform, ignoring how it emerges from the coupling between freight flows and technology adoption dynamics. Understanding thi...

MOAC-RT: Personalized multi-objective radiotherapy using actor-critic deep reinforcement learning

Adaptive rocket trajectory optimization via reinforcement learning and FPGA acceleration

This paper presents an integrated framework for reusable rocket trajectory optimization that combines deep reinforcement learning with FPGA-based inference acceleration. The proposed Adaptive Hardware-Accelerated Twin Delayed Deep Deterministic Policy Gradient (AHA-TD3) framework couples a hierarchical control policy,...

A Review of Neural Question Generation: Approaches, Challenges, and Future Directions

This survey provides a comprehensive review of developments in NQG, spanning traditional neural approaches to the latest paradigms driven by large language models (LLMs) and multimodal large language models (MLLMs).

VietLegalLM: Progressive Legal Expertise Through Synthetic Comprehension and Reinforcement Learning

This work presents vilaw-bench, a novel evaluation benchmark tailored for Vietnamese statute-based legal reasoning that assesses cognitive abilities from basic knowledge retrieval to complex legal interpretation, and proposes a multi-stage training framework that progressively builds legal expertise through four phases...

基于多智能体强化学习的软件开发流程优化

This paper investigates the application of Multi-Agent Reinforcement Learning (MARL) to optimize software development processes. Traditional software development methodologies often struggle with adaptability and efficiency, particularly in complex projects. This research proposes a novel approach leveraging MARL to dy...