reinforcement learning

Meta-Learning-Driven Deep Reinforcement Learning for Green Resource Management in 5G/6G Networks

Risk-Adaptive Behavioral Attenuation via Episode-Latched Policy Gating and Bounded Relearning for Deployed Reinforcement Learning Agents

Reinforcement learning behaviors can become harmful after deployment even when the policy is unchanged, a risk existing methods address mainly before or during training rather than through reversible correction. This paper proposes a risk-adaptive attenuation framework for a frozen context-conditioned policy encoding v...

Pareto optimization of textual and engagement features for crime-related social media analytics

Crime-related social media content provides valuable information for situational awareness and policy support; however, engagement-driven signals, such as likes and retweets, can introduce popularity bias and affect predictive outcomes. This study proposes a fairness-aware, Pareto-based, multi-objective optimization fr...

Robust Optimal Tracking Control for Center-of-Mass-Offset Mecanum Mobile Robots Using Off-Policy Integral Reinforcement Learning

An agentic AI framework with large language models and chain-of-thought for UAV-assisted logistics scheduling with mobile edge computing

In cloud manufacturing, unmanned aerial vehicles (UAVs) can support both product collection and mobile edge computing (MEC). This joint operation forms a hybrid scheduling problem, where physical logistics decisions are coupled with computational task scheduling. In this paper, UAVs collect finished products from manuf...

SteerCA: reinforcement learning environments for controlling cellular automata

SteerCA is a framework of reinforcement learning environments in which an agent steers a cellular automaton by acting on the cell it stands on, racing the automaton's own dynamics. Every action costs automaton time, so the cost of acting is a continuous difficulty axis set by construction. This release contains the Fir...

Reliabilityand Availability of a Repairable k-Out-of-n System via Event-Triggered Maintenance Semi-Markovian Modelling Reinforcement Q-Learning, and Copula Repair

This paper develops a reliability and availability model for a repairable two-tier k-out-of-n computer network system. Four analytical components are combined: event-triggered repair intensification, Gumbel–Hougaard copula-based restoration, a semi-Markovian state-transition formulation, and a reinforcement Q-learning...

Edge Intelligence and Adaptive Resource Management for Smart Services: Placement, Scheduling and Dispatch From Devices to the Cloud

Smart Services Such As Health Monitoring, Camera-Based Access Control And Ride-Hailing Must Decide What To Compute, Where To Compute It And When. Sending Every Reading To The Cloud adds Delay And Network Load, While Running Every Model On The Device Drains Its Battery And Memory. Edge Computing Places Processing Betwee...

Research on deep reinforcement learning-driven music melody generation algorithms

A novel hybrid melody-generation framework which integrates a Variational Autoencoder (VAE) with Reinforcement Learning (RL) through Proximal Policy Optimization (PPO) to enhance musical structure, coherence, and statistical expressiveness. For the purpose of providing a clean training corpus, the Lakh MIDI Dataset Cle...

Reinforcement learning guided robotic platform that investigates and discovers asymmetric reactions

Abstract Autonomous robotic platforms accelerate chemical discovery by coupling automated experimentation with algorithmic decision-making, but most lack integrated support for chirality and the sequential decision-making required to explore asymmetric reaction spaces with unknown reactivity and stereochemical outcomes...

ANIS Framework and Protocol Description (Version 1.0)

The Adaptive Neuroregulation Integration System (ANIS) is an integrative, neurobiologically informed framework designed to identify and modify patterns of physiological, cognitive, behavioral, and interoceptive responding that may contribute to persistent states of stress-related dysregulation. ANIS is based on the pre...

Integrated Drilling–Production Optimization Framework Using Reinforcement Learning and Digital Twin Simulation

Abstract As drilling becomes more complex and the need for intelligent use of drilling life cycles intensifies, the petroleum industry is embracing advanced digital technologies at a faster rate. Although there has been great advancement in the automation of drilling and the use of data to optimize drilling, production...