Tootfinder

REFN: A Reinforcement-Learning-From-Network Framework against 1-day/n-day Exploitations
Tianlong Yu, Lihong Liu, Ziyi Zhou, Fudu Xing, Kailong Wang, Yang Yang
https://arxiv.org/abs/2508.10701

REFN: A Reinforcement-Learning-From-Network Framework against 1-day/n-day Exploitations
The exploitation of 1 day or n day vulnerabilities poses severe threats to networked devices due to massive deployment scales and delayed patching (average Mean Time To Patch exceeds 60 days). Existing defenses, including host based patching and network based filtering, are inadequate due to limited scalability across diverse devices, compatibility issues especially with embedded or legacy systems, and error prone deployment process (manual patch validation). To address these issues, we introdu…

Safe Deep Reinforcement Learning for Resource Allocation with Peak Age of Information Violation Guarantees
Berire Gunes Reyhan, Sinem Coleri
https://arxiv.org/abs/2507.08653

Safe Deep Reinforcement Learning for Resource Allocation with Peak Age of Information Violation Guarantees
In Wireless Networked Control Systems (WNCSs), control and communication systems must be co-designed due to their strong interdependence. This paper presents a novel optimization theory-based safe deep reinforcement learning (DRL) framework for ultra-reliable WNCSs, ensuring constraint satisfaction while optimizing performance, for the first time in the literature. The approach minimizes power consumption under key constraints, including Peak Age of Information (PAoI) violation probability, tra…

Modeling revolutions in networked societies: learning from the Tunisian spring
Daniel Aguilar-Vel\'azquez, Denis Boyer, Robert Boyer
https://arxiv.org/abs/2508.06684 https:/…

Modeling revolutions in networked societies: learning from the Tunisian spring
Economic competition and deregulation have led to a polarization of societies between a small, increasingly powerful elite and a majority of socially excluded individuals, marginalized and unconnected to political representations. This is the breeding ground for protest movements, relayed by local ties and amplified by social networks. Based on the characteristics revealed by socio-economic research into the Arab revolutions of the 2010s, this article proposes a formalization inspired by the th…

Teachers of bachelors lab courses collaborating to promote open inquiry: a case study
Lesley G. A. de Putter, Marloes M. H. G. Hendrickx
https://arxiv.org/abs/2508.10379 https:/…

Teachers of bachelors lab courses collaborating to promote open inquiry: a case study
A group of university bachelors teachers of open inquiry lab courses in science subjects collaborated in a Networked Faculty Learning Community (NFLC) with the final goal to share their course materials publicly. In this paper we describe their collaborative process, communication on the topic and their road to a common definition of open inquiry as a didactical method. The study emerged organically during the collaboration of this NFLC as the group increasingly recognized the value of document…

Evolutionary Dynamics with Self-Interaction Learning in Networked Systems
Ziyan Zeng, Minyu Feng, Attila Szolnoki
https://arxiv.org/abs/2507.00422 https://…

Evolutionary Dynamics with Self-Interaction Learning in Networked Systems
The evolution of cooperation in networked systems helps to understand the dynamics in social networks, multi-agent systems, and biological species. The self-persistence of individual strategies is common in real-world decision making. The self-replacement of strategies in evolutionary dynamics forms a selection amplifier, allows an agent to insist on its autologous strategy, and helps the networked system to avoid full defection. In this paper, we study the self-interaction learning in the netw…

Distributed Neural Policy Gradient Algorithm for Global Convergence of Networked Multi-Agent Reinforcement Learning
Pengcheng Dai, Yuanqiu Mo, Wenwu Yu, Wei Ren
https://arxiv.org/abs/2505.24113

Distributed Neural Policy Gradient Algorithm for Global Convergence of Networked Multi-Agent Reinforcement Learning
This paper studies the networked multi-agent reinforcement learning (NMARL) problem, where the objective of agents is to collaboratively maximize the discounted average cumulative rewards. Different from the existing methods that suffer from poor expression due to linear function approximation, we propose a distributed neural policy gradient algorithm that features two innovatively designed neural networks, specifically for the approximate Q-functions and policy functions of agents. This distri…

Dynamic Evolution of Complex Networks: A Reinforcement Learning Approach Applying Evolutionary Games to Community Structure
Bin Pi, Liang-Jian Deng, Minyu Feng, Matja\v{z} Perc, J\"urgen Kurths
https://arxiv.org/abs/2506.17925

Dynamic Evolution of Complex Networks: A Reinforcement Learning Approach Applying Evolutionary Games to Community Structure
Complex networks serve as abstract models for understanding real-world complex systems and provide frameworks for studying structured dynamical systems. This article addresses limitations in current studies on the exploration of individual birth-death and the development of community structures within dynamic systems. To bridge this gap, we propose a networked evolution model that includes the birth and death of individuals, incorporating reinforcement learning through games among individuals. …

Power-Constrained Policy Gradient Methods for LQR
Ashwin Verma, Aritra Mitra, Lintao Ye, Vijay Gupta
https://arxiv.org/abs/2507.15806 https://

Power-Constrained Policy Gradient Methods for LQR
Consider a discrete-time Linear Quadratic Regulator (LQR) problem solved using policy gradient descent when the system matrices are unknown. The gradient is transmitted across a noisy channel over a finite time horizon using analog communication by a transmitter with an average power constraint. This is a simple setup at the intersection of reinforcement learning and networked control systems. We first consider a communication-constrained optimization framework, where gradient descent is applie…