reinforcement learning

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.

Research on Accurate Load Forecasting and Optimization Algorithm for Distribution Networks Based on Improved BiLSTM and DRL (High Load Density Areas)

An integrated algorithm based on improved Bidirectional Long Short-Term Memory and Deep Reinforcement Learning provides an effective solution for intelligent load prediction and adaptive energy management, offering practical support for electromagnetic energy distribution and resilient operation in next-generation smar...

Design of a Chinese Learning Platform Based on Knowledge Graph and Adaptive Question Answering

A Chinese learning platform integrating knowledge graph technology and adaptive question answering, developed to address fragmented knowledge structures, unclear learning paths, and insufficient personalized support in Chinese learning for non-native speakers is designed.

AoI-Guaranteed Dynamic Route Planning for Connected Vehicles

This paper presents a novel dual- factor approach that integrates travel time estimation and radio resource availability into an innovative route-planning scheme for connected vehicles (CVs) and shows that AGDRP outperforms the baseline scheme, which solely focuses on travel time optimization.

Bridging Ecological Inference and Decision Optimization for Conservation Using Artificial Intelligence

This work developed an adaptive decision model that selects production and distribution decisions of the supplementation program in response to the observed demographic, hydrological, and genetic environment and demonstrates that minimum sub-population size and total population size were primary drivers of the model’s...

Research on supply chain resilience-cost optimization joint decision making in multinational enterprise digital transformation based on multi-task deep reinforcement learning

The results suggest that MT-DRL can improve the simulated resilience-cost trade-off, while further validation with transparent datasets and real-world deployment tests remains necessary.

CloudSafe-TP: Hybrid AI-Based SLO-Aware Predictive Thread-Pool Control for Distributed Cloud Backends

Overall, CloudSafe-TP provides predictive and uncertainty-aware pool-size adjustment that improves P99 SLO protection during bursty workloads while avoiding unnecessary concurrency and resource usage under moderate load.

Do Reinforcement Learning Agents Improve Commodity Sector Rotation? Walk-Forward Evidence from Expert Selection, Strong Benchmarks, and a Frozen-Policy Temporal Extension

This paper tests whether reinforcement learning improves monthly commodity-sector rotation once the experiment is reconstructed from investable instruments, strong active benchmarks, realistic costs, and strictly chronological validation. Total returns for energy (DBE), gold (GLD), agriculture (DBA), and base metals (D...

Causal trust aware federated multi agent reinforcement learning for 6G edge networks