reinforcement learning

Study record and reproduction code for a survey of evaluation design and disclosure requirements in reinforcement learning-based route guidance

Reproduction material for the survey "Study-Area Boundaries in Reinforcement Learning-Based Route Guidance: A Survey of Evaluation Design and Disclosure Requirements" by Donghoun Lee. The package holds the study record of the 93 reviewed studies, one row per study with the sentence of the research article behind each r...

Subverting the Grinding Economy: An Edge-Based Autonomous Deep Reinforcement Learning Agent and Humanized Behavioral Biometrics in Mobile Gaming

Modern mobile freemium games increasingly rely on coercive engagement loops and artificially inflated grinding mechanics—a socio-technical phenomenon conceptualized as digital "playbor"—to drive in-app purchases and ad monetization. To protect these revenue streams, commercial game publishers deploy sophisticated behav...

Visual swarm navigation via deep reinforcement learning and evolutionary hybrid design

Swarm robotics presents a robust and cost-effective paradigm for advanced automation in complex, dynamic environments, such as those encountered in search and rescue or environmental monitoring. A fundamental challenge for this field is the data-driven design of decentralized controllers capable of generating emergent...

Distance-Aware Inverse Reinforcement Learning Dataset for Robotic Grasping

This dataset contains state-action observations collected from a low-cost robotic manipulator during expert-guided demonstrations for robotic grasping. Each observation records four joint measurements, the scanning-servo angle of the time-of-flight (ToF) distance sensor, the measured distance between the robot and the...

Training ML Models to Adapt at Test Time

Modern machine learning systems concentrate most of their computation beforedeployment and then use the resulting model as a fixed predictor. This design is brittle when deployment differs from training and poorly matched to difficult problems whose solution benefits from additional reasoning, feedback, or interaction...

Adaptive Computational Framework for Autonomous Decision Optimization Using Reinforcement-Based Neural Systems

A Granger causality-guided multi-graph transformer framework for traffic flow forecasting

Accurate traffic forecasting is challenging because of the difficulty of capturing time-varying propagation and multi-scale spatio-temporal interactions. Most existing deep learning models only learn correlations rather than directional dependencies, which limits interpretability and robustness under dynamic traffic co...

An online approach for integrated railway schedule replanning and passenger reassignment with unknown passenger reactions under hub disruptions

Major railway disruptions, such as city-wide disasters, in hub areas often persist for a long time and affect multiple routes simultaneously. Original railway schedules, including line plans, timetables, and rolling stock dispatching, become invalid and require strategic replanning to maintain serviceability. Under suc...

Runtime Attribution in PPO with Predictive Action Projection: An Independent-Run Fixed-to-Transfer Audit

This paper examines how to evaluate a reinforcement-learning controller whose proposed actions are modified by an online projector. Using PPO in simulated constrained navigation, it focuses on distinguishing the performance of the policy evaluated without projection from that of the complete controller operating with p...

Reinforcement Learning-Based Compliant Locomotion for Sensorless Humanoids in Physical Human-Robot Interaction with Sim-to-Real Force Dispersion Compensation

Contact Force Estimation-Guided Reinforcement Learning Framework for Compliant Humanoid Bipedal Walking

Federated reinforcement learning-based cooperative operation of community microgrids with electric vehicle charging and swapping stations using asymmetric Nash bargaining