reinforcement learning

A Maximum Entropy Multi-agent Reinforcement Learning Algorithm Based on Gated-Self-attention

AI-Based Dynamic Pricing in Digital Commerce: Implications for Consumers and Retailers

Artificial intelligence (AI) is reshaping pricing in digital commerce. Retailers can now combine demand, inventory, competitor prices and customer signals to revise prices quickly. This review examines how AI-based dynamic pricing works and what it means for consumers and retailers. It separates market-responsive prici...

Failure-resilient narrative-coherent sports video summarization under multimodal asynchrony: a context-aware swin reinforcement learning framework

Design, management, and optimization of networks using machine learning, deep learning, and reinforcement learning

Reinforcement learning-based optimized search for extraterrestrial intelligence (SETI)

Adaptive operator selection via deep reinforcement learning for decomposition-based multi-objective multi-task optimization

Abstract Multi-objective multi-task optimization (MO-MTO), which aims to simultaneously solve multiple related multi-objective problems by leveraging knowledge transfer across tasks to enhance optimization performance on each task, has emerged as a new research focus in the field of evolutionary computation. Although a...

Multi-axis CNC machining and toolpath strategies for complex free-form geometries: a systematic review of geometry, kinematics, process physics and intelligent optimization

In addition to offering the enabling process for the turbine blade, blisks, impellers, moulds, dies and other free-form components, multi-axis computer numerical control (CNC) machining introduces the geometry of toolpath, kinematics of machine, cutting mechanics, surface integrity and energy use. In this paper, resear...

Memory formation as selection: an eligibility-based architecture

Memory is often discussed in terms of encoding strength, retention, and retrieval success. Yet these dimensions do not fully explain how remembered information acquires structure within a larger mnemonic system. Strongly retained or highly accessible traces need not be broadly integrated, and their persistence or vivid...

Sequential–hierarchical coordination for multi-task enroute air traffic control

Enroute air traffic controllers (ATCOs) face two fundamentally distinct yet interrelated operational tasks: macroscopic flow-level planning and flight level assignment for sector-wide traffic optimization over time horizons on the order of tens of minutes and microscopic real-time conflict detection and resolution (CD&...

Reinforcement learning for sustainable construction scheduling: a many-objective framework with adaptive search and multi-criteria decision support

Purpose Delivering construction projects sustainably requires reconciling duration, cost, carbon emissions and the stability and utilization of site resources, yet these objectives conflict and the choice among candidate schedules is seldom made on an explicit, data-driven basis. This study aims to develop a data-drive...

Radar-vision-V2X fused perception based intelligent control mechanism for service-oriented traffic signals

Traditional traffic signal systems lack the capability to perceive vehicle types and real-time traffic flow dynamics, particularly in identifying priority levels for emergency vehicles. To address this, an intelligent traffic flow decision-making and scheduling system based on radar-visual-V2X(Vehicle-to-Everything) mu...

Scooter-Driving Humanoid Robot: Sim-to-Real Transfer Through Deep Reinforcement Learning

This paper presents a simulation-to-reality (sim-to-real) transfer process, allowing a full-sized humanoid robot to autonomously balance a two-wheeled scooter and track operator-supplied heading commands through a deep reinforcement learning (DRL) policy, including while carrying a human passenger for the first time. T...