Stock Price Prediction, Deep Learning, LSTM, GRU, Attention Mechanism, Financial Time Series Share and Cite: Kirui, D. (2026) ...

As the electricity market is progressively liberalized, virtual bidding has emerged as a novel participation mechanism attracting increasing attention. This paper integrates evolutionary game theory ...

Utilities worldwide are turning to artificial intelligence (AI) and machine learning to stabilize networks, forecast consumption, detect faults, and optimize system performance in real-time. What was ...

Abstract: Reinforcement learning algorithms have revolutionized autonomous decision-making in various domains. In this paper, we compare Q-learning and DQN for solving a 100x100 grid model of a ...

In this tutorial, we explore how exploration strategies shape intelligent decision-making through agent-based problem solving. We build and train three agents, Q-Learning with epsilon-greedy ...

Notch is a reinforcement learning implementation that demonstrates Q-Learning algorithms for autonomous pathfinding in obstacle-rich grid environments. The system employs temporal difference learning ...

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 ...

Aiming to address the complexity and uncertainty of unmanned aerial vehicle (UAV) aerial confrontation, a twin delayed deep deterministic policy gradient (TD3)–long short-term memory (LSTM) ...

This important study uses reinforcement learning to study how turbulent odor stimuli should be processed to yield successful navigation. The authors find that there is an optimal memory length over ...

Abstract: This study explores the use of supervised learning and Q-learning to optimize dynamic preamble allocation in a single-cell 5G network, supporting SDG 9 (Industry, Innovation, and ...