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...

Deep reinforcement learning-based optimal multi-energy dispatch in a droop-controlled islanded microgrid

Optimized AI‐Driven Intelligent Transportation Systems for Secure Communication and Traffic Management in Post‐5G Networks

ABSTRACT After 5G vehicular networks, an intelligent transportation system with an AI‐based framework encompasses the real‐time traffic management, security, and quality of service for safer communication. This paper proposes a new comprehensive framework for improving vehicular network performance based on state‐of‐th...

Empowering a first-time mother through postnatal education is not just teaching newborn care—it is nurturing confidence, protecting a precious life, and building a healthier future for generations

The first weeks after childbirth are a period of rapid learning, physical recovery, emotional adjustment, and relationship building. For a first-time mother, everyday activities such as feeding, holding, bathing, comforting, protecting warmth, interpreting crying, recognising illness, and deciding when to seek professi...

SeedPro: Place Brachytherapy Seeds Like Expert Clinicians via Hierarchical Reinforcement Agents

Geometric Shielding and Lagrangian Constraints in Safe Reinforcement Learning: A Factorial Study

Safe reinforcement learning requires balancing task performance against safety constraints, yet how different safety mechanisms interact when combined remains an open question. We present a systematic empirical comparison of four reinforcement learning algorithms, PPO, SAC, RCPO (with fixed λ), and Lagrangian PPO (LagP...

Enhancing Pathological VLMs with Cross-Scale Reasoning

StructRL: Recovering Dynamic Programming Structure from Learning Dynamics in Distributional Reinforcement Learning

Reinforcement learning is typically treated as a uniform, data-driven optimization process, where updates are guided by rewards and temporal-difference errors without explicitly exploiting global structure. In contrast, dynamic programming methods rely on structured information propagation, enabling efficient and stabl...

The need for uncertainty: why probabilistic LSTMs are key to improving flood predictions and enabling learned warning rules

Code and results accompanying the EGUsphere preprint egusphere-2026-469. This repository implements deep-learning rainfall–runoff models over the CAMELS-CH basins, comparing a deterministic LSTM baseline against three probabilistic output heads — a DRN model (called LD-LSTM in the code), a Bernstein Quantile Network (B...

Microgrid energy management optimization using PPO with curriculum learning trained in virtual simulation environment

Microgrid energy management systems (EMS) require real-time, economically optimal, and safe dispatch strategies under high renewable uncertainty. While Deep Reinforcement Learning (DRL) offers promising online decision-making capabilities, standard DRL algorithms struggle with the “cold start” problem, slow convergence...

Long-term engagement-oriented short video recommendation via deep reinforcement learning

Short video platforms require recommendation models that can adapt to rapidly changing user interests while optimizing engagement beyond immediate click feedback. Existing collaborative filtering and supervised deep learning methods are effective for static preference matching and short-term click-through-rate predicti...

Strategic flight planning and conflict management for urban air mobility operations: a mission preference-constrained multiagent reinforcement learning approach

As an emerging low-altitude transportation paradigm, urban air mobility (UAM) is envisioned to support high-density and demand-driven operations involving diverse and flexible mission requests. However, the imbalance between limited urban airspace resources and growing operational demands inevitably causes frequent fli...