An Ensemble Learning Tool for Land Use Land Cover Classification Using Google Alpha Earth Foundations Satellite Embeddings ...

AI researcher Anmol Aggarwal explains how fairness-aware pricing algorithms can reduce hidden bias without major revenue loss ...

In an RL-based control system, the turbine (or wind farm) controller is realized as an agent that observes the state of the ...

Unmanned surface vehicles (USVs) nowadays have been widely used in ocean observation missions, helping researchers to monitor climate change, collect environmental data, and observe marine ecosystem ...

The Recentive decision exemplifies the Federal Circuit’s skepticism toward claims that dress up longstanding business problems in machine-learning garb, while the USPTO’s examples confirm that ...

Discovering faster algorithms for matrix multiplication remains a key pursuit in computer science and numerical linear algebra. Since the pioneering contributions of Strassen and Winograd in the late ...

ABSTRACT: Offline reinforcement learning (RL) focuses on learning policies using static datasets without further exploration. With the introduction of distributional reinforcement learning into ...

Abstract: Q-learning and double Q-learning are well-known sample-based, off-policy reinforcement learning algorithms. However, Q-learning suffers from overestimation bias, while double Q-learning ...

Institute of Logistics Science and Engineering of Shanghai Maritime University, Pudong, China Introduction: This study addresses the joint scheduling optimization of continuous berths and quay cranes ...

Reinforcement learning (RL) trains agents to make sequential decisions by maximizing cumulative rewards. It has diverse applications, including robotics, gaming, and automation, where agents interact ...