reinforcement learning

Data-driven bike-sharing rebalancing for smart cities: Reinforcement learning, operational constraints, and service-cost trade-offs

A risk-adaptive deep reinforcement learning approach for energy management of fuel cell hybrid electric vehicles

Enhancing PLS in UAV-Aided Backscatter Networks: A Multiagent DRL Framework with Federated Learning

In recent years, enhancing physical layer security (PLS) in UAV-aided backscatter networks has received huge attention among the research community due to its various applications, such as smart farming, cognitive healthcare, and intelligent industrial automation, among others. It results in secure and reliable data tr...

Research on asymmetric stochastic advertising differential games based on multi-agent deep reinforcement learning

Optimization of Underground Sublevel Stoping Layouts Considering Geological and Grade Uncertainty Using Deep Reinforcement Learning

From Power Concentration to Structured Light Generation: Scalable, Efficient and Generalizable Coherent Beam Combination With Model‐Based Reinforcement Learning

This work proposes a model‐based reinforcement learning (MBRL) framework that enables intelligent phase control by effectively emulating CBC system dynamics via a physics‐informed environment model, and establishes a powerful paradigm for integrating MBRL into high‐power optical field modulation.

Combating Web-Based E-Commerce Fraud Syndicates: Fairness-Aware Hypergraph Contrastive Fraud Detection with Multi-Dimensional Reinforcement Rewards

MRC, a novel semi-supervised model that integrates Multi-view Heterogeneous Hypergraph Contrastive learning (MHHC) and Multi-Dimensional Reinforcement reward driven Community detection (MDRC) for web-based e-commerce collusive fraud detection, is proposed.

Metanormative Theory for RL-Based Moral Agents

This paper aims to draw out ideas from recent work in metanormative theory that can be useful for designing artificial moral and value-aligned agents and examine the RL architecture through the lens of these ideas.

MoE-Pointer: Seq2Seq Reinforcement Learning for Dynamic Multi-Echelon Pickup-and-Delivery with Courier-Drone Relay

MoE-Pointer is proposed, a unified reinforcement learning framework that reformulates DM-PDP into a sequence-to-sequence generation task and introduces a Prior-Guided Soft Mask to guide exploration within the exponentially large action space.

Graph Attention Reinforcement Learning with Electrical Prior Knowledge for Distribution System Restoration

Case studies on the IEEE 34-bus system demonstrate that the proposed graph attention-based coupling-aware reinforcement learning method outperforms benchmark algorithms in training convergence, restored power, and online decision efficiency, enabling fast and effective distribution system restoration.

Left Turns at Unsignalized Intersections: Decision Making for Autonomous Vehicles Using Deep Reinforcement Learning with Game-Theoretic Reasoning

Results show that incorporating game-theoretic reasoning substantially improves decision stability, efficiency, and safety compared with a baseline DRL agent, achieving higher rewards, shorter maneuver times, and zero collisions across all scenarios.

Improving the Accuracy of Multi-Dimensional Cost Allocation in Public Hospitals Using a GNN-RL Hybrid Strategy Discrete Optimization Algorithm

A hybrid strategy discrete optimization algorithm based on Graph Neural Networks (GNN) and Reinforcement Learning (RL) that mines the complex correlation features between cost elements through GNN and uses RL to realize the learning of optimal allocation strategies in dynamic environments.