reinforcement learning

A Reward-Driven Decision Optimization Method for Biological Simulation

Self-Programming Artificial Intelligence: Autonomous Learning and Evolutionary Algorithms

This paper presents a hybrid framework for self-programming artificial intelligence (AI), integrating reinforcement learning (RL), genetic programming (GP), neural architecture search (NAS), and meta-learning to enable autonomous code evolution with minimal human intervention. The proposed system is designed to optimiz...

Editorial: Advancing sustainability and resilience in agri-food supply chains through multi-criteria decision-making methods

Agri-food supply chains (AFSCs) are found at the heart of many of the major challenges faced today, such as climate variability, geopolitical instability, digital disruption, and increased customer expectations for healthy, sustainably produced, yet affordable foods. In particular, smalland medium-sized enterprises (SM...

Safe reinforcement learning for aggregated demand response control of emerging flexible resources in active distribution networks

DeepRL-OLSR: A Deep Reinforcement Learning based Routing Mechanism for Energy-Efficient Data Transmission in WSN

Bridging Offline and Online Multi-Objective Reinforcement Learning

Bio-inspired exploration of butterfly wing morphology guides deep reinforcement learning optimization of robotic flight

How wing morphology shapes flight performance is a central question in functional biology and bio-inspired robotics, yet these relationships are difficult to isolate in living organisms. Here, we use a robotic butterfly with tunable wing geometry to systematically characterize morphology–performance relationships by co...

Bayesian Black-Litterman under Nonlinear Dependence: A Hybrid Framework with GARCH-EVT Marginals, R-Vine Copulas, and Reinforcement Learning for Tactical Portfolio Allocation

Generative Artificial Intelligence-Driven Enterprise Knowledge Base

As enterprises generate vast amounts of heterogeneous knowledge, existing knowledge management systems face challenges in providing contextually relevant, accurate, and domain-specific answers. To address these issues, the authors propose KQGen, a unified framework that integrates knowledge graph embeddings with genera...

Multiagent rendezvous in fluid flows via reinforcement learning

Delay-aware multi-agent reinforcement learning for mixed-autonomy platoon control

It is recognized that controlling mixed-autonomy platoons comprising connected and automated vehicles (CAVs) and human-driven vehicles (HDVs) can enhance traffic flow. While multi-agent reinforcement learning (MARL) is a promising real-time control paradigm, existing MARL-based platoon controllers rarely account for co...

Intelligent planning and scheduling system for supply chain logistics routes based on reinforcement learning

The optimization of logistics routing and scheduling in supply chains is a critical challenge, particularly in dynamic environments characterized by fluctuating demand, traffic conditions, and stringent time-capacity constraints. Traditional optimization methods and heuristic-based approaches often struggle to adapt an...