reinforcement learning

Generalization from early to middle childhood

Generalization enables individuals to apply knowledge derived from past experiences in novel situations, yet it is unclear whether common experimental paradigms probe the same underlying ability in early to middle childhood. We assessed 84 children aged 4, 6, and 8 years on four paradigms, namely statistical learning,...

EdgeEngine AWARE: an application- and energy-aware simulation environment for reinforcement learning in energy-harvesting Edge IoT systems

A Gymnasium environment in which an agent schedules the sensing and the transmissions of an energy-harvesting embedded node from hardware-measurable quantities only. Three application domains (smart agriculture with a solar panel, indoor air quality with an indoor photovoltaic cell, industrial motor monitoring with a t...

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...

Dynamic Potential Field Exploration Framework: An Internal Value Construction Method Based on State Visitation History

Effective exploration in sparse-reward environments is one of the important challenges in reinforcement learning. Existing methods typically rely on external reward signals or intrinsic motivation mechanisms derived from rewards to establish behavioral value. However, when environmental feedback is extremely limited, a...

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...