reinforcement learning

Dynamic Topological Dependency Graph Learning (DTTL)

This paper introduces Dynamic Topological Dependency Graph Learning (DTTL), a novel approach to program dependency analysis that leverages reinforcement learning to automatically construct and refine dependency graphs without manual intervention. Traditional static dependency analysis tools are limited in their ability...

基于多智能体协作的复杂系统模拟

This paper presents a novel approach to simulating complex systems by leveraging the power of multi-agent collaboration. The core idea is to construct a population of intelligent agents, each with varying levels of cognitive ability, and task them with solving complex problems collectively. Reinforcement learning is em...

Dynamic Resource Allocation in Serverless Architectures via Reinforcement Learning

Serverless computing architectures offer significant advantages in terms of scalability and cost-efficiency. However, traditional serverless resource allocation strategies often rely on static configurations or simple heuristics, which can lead to suboptimal performance and increased costs. This paper proposes a novel...

Development of a Collective Intelligence Framework for Optimising Demand and Supply-Side Smart Energy Management to Support Net-Zero Energy Outcomes in the Built Environment

Achieving net-zero energy in residential precincts remains challenging due to diverse energy demand patterns, intermittent renewable energy generation, and the need to balance energy demand, supply, thermal comfort, and affordability. Existing studies have focused on optimising demand and supply to reduce grid energy c...

Dynamic Topological Dependency Reasoning Network (DTTRN)

This paper introduces the Dynamic Topological Dependency Reasoning Network (DTTRN), a novel neural architecture designed for real-time learning and adaptation of complex system topology dependencies. Traditional dependency analysis methods rely on static, pre-defined topologies, limiting their effectiveness in dynamic...

##基于生物反馈的机器学习模型优化

This paper explores the novel approach of utilizing biofeedback signals, specifically electroencephalogram (EEG) and electrocardiogram (ECG) data, to optimize the performance of machine learning models. The core idea is to leverage the dynamic and often subtle information contained within these physiological signals as...

Adaptive Recursive Neural Network Architecture

This paper proposes a novel adaptive recursive neural network (ARNN) architecture designed to overcome the limitations of traditional, static recurrent neural networks. The core idea is to introduce dynamic adaptation of the network's recursive structure, leveraging evolutionary algorithms or reinforcement learning, to...

Traffic road control and lane allocation framework for intelligent transportation system using federated deep reinforcement learning

A Cognitive Architecture-Based General Artificial Intelligence Model

This paper proposes a novel approach to developing General Artificial Intelligence (GAI) by leveraging cognitive architectures. The core idea is to model human cognition—encompassing perception, memory, reasoning, learning, and decision-making—within a computational framework. This architecture is built upon a modular...

Automated Theorem Proving with Deep Reinforcement Learning

Automated theorem proving (ATP) has achieved significant progress over the years, yet it frequently struggles with complex mathematical reasoning and strategic planning. This work introduces a novel approach to ATP leveraging deep reinforcement learning (DRL). The core idea is to train an intelligent agent capable of i...

Deep reinforcement learning for melt pool solidification cooling rate control in directed energy deposition

##基于强化学习的分布式系统资源调度

This paper investigates the application of Reinforcement Learning (RL) for dynamic resource scheduling in distributed systems. Traditional resource scheduling methods often rely on static rules or heuristics, which may not adapt effectively to the fluctuating demands and workloads of a distributed environment. This res...