reinforcement learning

A Nonasymptotic Theory of Seminorm Lyapunov Stability: From Deterministic to Stochastic Iterative Algorithms

We study the problem of solving fixed-point equations for seminorm-contractive operators and establish foundational results on the nonasymptotic behavior of iterative algorithms in both deterministic and stochastic settings. In the deterministic setting, we present a fixed-point theorem for seminorm-contractive operato...

Hybrid deep reinforcement learning-assisted adaptive TID controller for sensorless BLDC motor drives with torque ripple minimization and dynamic stability enhancement

A reinforcement learning-guided breakout local search algorithm for the heaviest subgraph problem

TamRL - Less Effort, Shorter Proofs: Reinforcement Learning for Security Protocol Analysis in Tamarin - Artifact Data

This artifact contains result data and models for the paper Less Effort, Shorter Proofs: Reinforcement Learning for Security Protocol Analysis in Tamarin. Corresponding code can be found at https://github.com/MatCos/TamRL for the Reinforcement Learning Framework, and at https://github.com/niklasmedinger/Tamarin-ML-API...

Self-Configuring Hierarchies with Meta-Adaptive Hierarchical Reinforcement Learning for Multi-Agent cooperative Control

Solves the rigidity of manual hyperparameter tuning in traffic control using Meta-MOHRL, a self-configuring HRL framework for optimizing unpredictable multi-objective traffic flows. A PyTorch framework enabling multi-objective traffic coordination. Includes SUMO environment & topologies, and SoTA hierarchical baselines...

Camouflage-resistant graph neural networks for power grid anomaly detection

Power grid anomaly detection requires modeling both network topology and device dependencies. Graph neural networks (GNNs) are suitable for this task. However, few prior works have considered the stealthy camouflage behavior of grid anomalies, where faulty devices or malicious attackers intentionally mask their abnorma...

An Intelligent Deep Reinforcement Learning Framework for Cost-Efficient and Energy-Aware Cloud Resource Management

The rapid growth of cloud computing and Internet of Things (IoT) applications has intensified the need for efficient resource allocation, energy management, and cost optimization in large-scale data centers. Traditional optimization and machine learning approaches, while effective in static environments, often fail to...

Learning human visual attention on 3D surfaces through geometry-queried semantic priors

Hybrid digital twin and reinforcement framework for intelligent network slicing and dynamic resource allocation in 5G edge computing environments

In the world of rapidly evolving communication networks, network demand and traffic are increasing, which makes efficient network slicing and resource allocation techniques a requirement. The existing models for network slicing and resource allocation lack efficient algorithms, limited generalization, and practical imp...

Towards reliable LLM evaluators: Discovering and internalizing explicit scoring logic

Large language models (LLMs) have become effective evaluators across a wide range of tasks, but in rigorous contexts such as educational measurement, a model that can produce plausible scores is not yet a reliable rater. A central limitation of current LLM evaluators is that their scoring logic remains implicit and is...

Fast finite-time adaptive optimal algorithm for stochastic multiagent systems under deception attacks and time-varying output constraints

An adaptive optimal neural network algorithm with fast finite-time convergence is proposed for stochastic multiagent systems (SMASs) under deception attacks, time-varying asymmetric output constraints and dead zones. An additional attack signal corrupts the state information of nonlinear systems, resulting in the unava...

Offline and offline-to-online reinforcement learning for bus holding control

Abstract Bus bunching is a self-reinforcing phenomenon in which a delayed bus boards more passengers, incurs further delay, and is caught by its follower. Holding a bus at a stop to regulate headways is a widely studied corrective measure, yet most evaluations rely on a single shaped objective rather than independent p...