reinforcement learning

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...

Title: Dynamically Reconfiguring Quantum Circuit Optimization

Quantum computing holds immense promise for revolutionizing various fields, but the design and optimization of quantum circuits remain a significant challenge. Current methods often rely on manual tweaking and are limited by the time and expertise required. This paper introduces a novel system for dynamically reconfigu...

Title: Adaptive Quantum State Collapse for Quantum Simulation

Quantum simulation, the process of mimicking the behavior of quantum systems, holds immense promise for advancing scientific discovery in fields ranging from materials science to drug discovery. However, traditional quantum simulation methods often face challenges related to computational cost and fidelity, particularl...

Dynamic Topological Semantic Network Inference

This paper proposes a novel approach to system management leveraging dynamic topological information for intelligent inference. The core idea is to construct and maintain a semantic network that reflects the evolving relationships between system components, considering factors like network latency, device load, and com...

##基于多智能体强化学习的系统优化

This paper investigates the application of multi-agent reinforcement learning (MARL) for optimizing complex systems. Traditional system optimization approaches often rely on centralized control or distributed control strategies, which can struggle with the inherent complexity and dynamic nature of many real-world syste...