reinforcement learning

Tax Knowledge, Tax Literacy, Learning Motivation, and Students’ Tax Understanding

This study examines how tax knowledge, tax literacy, and learning motivation relate to tax understanding among tax accounting students at Politeknik Negeri Lampung. Primary data from 122 students in the 2022 and 2023 cohorts, selected through purposive sampling, were collected using a 16-item Likert-scale questionnaire...

Explainable Multi-Agent Task Scheduling in Vehicular Edge Computing Using Fuzzy Logic and Interpretable Deep Reinforcement Learning

Learning-enabled collaborative intelligence in search and rescue robotics: a survey of paradigms, models, and challenges

Search and Rescue (SAR) robotics increasingly depends on collaborative intelligence to operate effectively in dynamic, uncertain, and time-sensitive environments. Modern SAR missions require coordinated interaction among heterogeneous robotic agents as well as structured collaboration between humans and intelligent sys...

Artificial Intelligence and Machine Learning for Stock Market Prediction: A Systematic Literature Review of Methods, Emerging Paradigms, Reproducibility, and Evaluation Gaps (2016-2026)

The successor representation in high-risk drinking and alcohol-related contexts

The successor representation (SR) is a reinforcement learning strategy which has been suggested to underlie nuanced forms of habitual behavior, rendering it a candidate mechanism for mental health conditions thought to involve habitual control, such as addiction. However, paradigms used to assess habits in humans canno...

Crosswalk Task: A naturalistic reinforcement learning paradigm for characterizing real-world risky behavior

Physics-Informed Reinforcement Learning for Nonlinear Dynamical Systems

RIS-Assisted Covert Communication for Millimeter-Wave MIMO Channels in Railway Internet of Things

As the railway industry undergoes digital transformation driven by AI, big data, and 5G/6G, secure data transmission for IoT-oriented railway operations has become a critical challenge in high-speed railway (HSR) wireless communications. Covert communication, which conceals wireless transmissions from eavesdroppers, to...

Multiscale Simulation Algorithms for Understanding and Steering Complex Chemical Systems

Atomistic simulations and macroscopic continuum models often fall short in capturing the full complexity of chemical systems across disparate time and length scales. This dissertation bridges these domains through the development and application of physics-based and data-driven multiscale methods, progressing from the...

RNADaemon: Reinforcement Learning-Guided Molecular Dynamics for RNA Tertiary Structure Prediction

Abstract Accurate prediction of RNA tertiary structure remains a central challenge in structural biology, yet existing computational approaches face fundamental limitations arising from the ruggedness of the RNA free-energy landscape and the scarcity of experimentally resolved structures. Here we present RNADaemon, a f...

Replication package: regime-conditional prediction-decision coupling in uncertainty-aware deep reinforcement learning for trading systems

This record contains the complete replication material for a study of how strongly adistributional prediction model should be coupled to a sequential decision policy in low-signal,non-stationary markets. The manuscript evaluates fifteen coupling-mechanism configurations acrossthree walk-forward windows on five indices...

Role of AI in Thermal Control for Power Plants: Techniques, Challenges and Opportunities

INTRODUCTION: Power plants require efficient thermal control to reduce operational costs and improve energy performance. Conventional methods may have limited adaptability to changing operating and environmental conditions. AI-based techniques offer a promising solution for improving thermal efficiency and predictive m...