reinforcement learning

A 3D Network of Novel Nodes Sum Rate Maximization for 6G BD-RIS-Assisted Multi-Cell Transportation

A deep reinforcement learning–based joint orchestration algorithm (DRL-JOA) employing graph attention networks is proposed to solve the sum rate maximization problem in beyond diagonal reconfigurable intelligent surface (BD-RIS)-assisted multicell transportation networks within a space-air-ground integrated network fra...

A Multi-Resource Coordinated Scheduling Optimization Framework Integrating Berth Allocation, Quay Crane Operation, and AGV Transportation in Automated Container Terminals

This study investigates the operations in automated container terminals, in which scheduling decisions need to be generated automatically. Therefore, an integrated optimization model is established considering the two-way transportation process of containers between the berth and the yard with the assignment and schedu...

PREreview of "What Does an LLM Learn from Reinforcement Learning? A Mechanistic Interpretability Perspective with Fixed-SAE Track"

This Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at https://prereview.org/reviews/22775771. Summary The authors tackle a real obstacle in interpretability: you can't compare SAE features across training checkpoints, because separately trained dictionaries assign...

Multi-agent cooperative control for unmanned distributed-drive electric agricultural vehicle in paddy fields: tracking, stability, and energy-aware torque allocation

With the rapid advancement of intelligent agriculture and autonomous field operations, the distributed drive electric plant protection vehicle (DDEPPV) is increasingly adopted for paddy-field plant protection. However, in soft-soil, low-adhesion, and highly disturbed environments, the tight coupling among path tracking...

Spatio-Temporal Graph Transformer MARL for Resilient Point of Common Coupling Control in Networked Grid-Tied Micro-grids

The increasing penetration of renewable energy sources introduces significant variability and uncertainty into grid-connected microgrids (MGs), affecting voltage stability, frequency regulation, power quality, fault ride-through capability, and operational resilience at the Point of Common Coupling (PCC). These challen...

Performance anxiety is associated with uncertainty-dependent reward and punishment learning biases in skilled performers

Performance anxiety (PA) is common in high-stakes settings, from public speaking to the performing arts, yet its mechanisms remain poorly understood. Using behavioural analysis, computational modelling, and electroencephalography, we examine whether PA is associated with differences in learning from reward and punish...

UETO: The Scaling Cost Ceiling of Reinforcement Learning from Human Feedback

The scaling cost ceiling of RLHF and autonomous falsification via the Universal Intervention Barrier and the Universal Entropy-Threshold Operator. One of 135 dated defensive publications in the CatQualia record. Collection DOI: https://doi.org/10.5281/zenodo.22751107 Original publication: https://catqualia.com/ueto

GustaveKateb0709/aedes-rl-control-guangzhou: V2.0.0

Code and data: deep reinforcement learning for weekly mosquito-control allocation under an operational capacity ceiling This package reproduces every number, table and figure in the manuscript. Version 2.0.0 extends the original submission with a weekly operational-capacity ceiling, additional algorithms, cross-year va...

Deep reinforcement learning discovers a novel control algorithm for mitigating flow-induced vibrations in underactuated tandem cylinders

Decentralized Multi-Agent Reinforcement Learning for Optimizing Quantum Memory Based Quantum Repeaters

This repository implements a memory-based quantum repeater chain as a Gymnasium and PettingZoo environment, formulated as a multi-agent Partially Observable Markov Decision Process (POMDP). A Multi-Agent Reinforcement Learning (MARL) system based on the Proximal Policy Optimization (PPO) algorithm is trained to dynamic...

RoBCtrl: Attacking GNN-Based Social Bot Detectors via Reinforced Manipulation of Bots Control Interaction

Social networks have become a crucial source of real-time information for individuals. The influence of social bots within these platforms has garnered considerable attention from researchers, leading to the development of numerous detection technologies. However, the vulnerability and robustness of these detection met...

Single-Agent Reinforcement Learning Model for Regional Adaptive Traffic Signal Control

Several studies have employed reinforcement learning (RL) to address the challenges of regional adaptive traffic signal control (ATSC) and achieved promising results. In this field, existing research predominantly adopts multi-agent frameworks. However, the adoption of multi-agent frameworks presents challenges for sca...