Reinforcement learning (RL)
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Agentic AI: Architectures, Types, Capabilities, Mathematical Equations and Governance in the Era of Autonomous Intelligence
Abstract: Agentic Artificial Intelligence (Agentic AI) represents a major advancement in the evolution of intelligent systems by enabling autonomous planning, decision-making, and action execution. Unlike traditional AI models, which are primarily reactive and designed to respond to predefined inputs, Agentic AI systems possess capabilities such as memory, reasoning, goal-oriented planning, tool integration, and dynamic adaptation to changing environments. These characteristics allow them to perform complex, multi-step tasks with minimal human intervention, …
Published in Recent Trends in Mathematics · Vol. 3, Issue 2, 2026 Read article
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A Detailed Review on Intelligent and Robust Control Strategies for Autonomous Underwater Vehicles with Emphasis on Navigation, Path Tracking, and Stability Enhancement
Abstract: Autonomous Underwater Vehicles (AUVs) have gained significant attention due to their applications in ocean exploration, underwater surveillance, environmental monitoring, and offshore industries. The control of AUVs presents various challenges due to the highly dynamic and uncertain underwater environment, nonlinear hydrodynamics, and external disturbances. This review paper explores various control strategies employed for AUVs, including classical control methods such as Proportional-Integral-Derivative (PID) controllers, modern techniques like Model Predictive Control (MPC), and …
Published in International Journal of Electronics Automation · Vol. 3, Issue 2, 2025 · pp. 28–52 Read article
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Improvement of Convergence Speed of Q-learning based Path Planning Algorithm
Abstract: Path planning is fundamental and important task of mobile robot. There are many attempts to adopt reinforcement learning (RL) in mobile robot path planning. RL based path planning is effective in path planning of intelligent mobile robot, especially in unknown environment because it doesn’t require environmental information and finds optimal path through trial-and-error process. Q-learning is one of RL algorithm widely used in path planning of mobile robots. The main …
Published in International Journal of Advanced Robotics and Automation Technology · Vol. 2, Issue 2, 2024 · pp. 19–28 Read article
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Reinforcement Learning in Real World Application: A Study on Robotics; Autonomous Vehicles and Industrial Automation
Abstract: This research paper investigates the practical application of reinforcement learning (RL) in three critical domains: robotics, autonomous vehicles, and industrial automation. The study delves into the implementation of RL algorithms to enhance decision-making, adaptability, and autonomy in these real-world scenarios. Through a comprehensive review of existing literature, methodologies, and case studies, the paper addresses the challenges faced and the successes achieved in deploying RL in each domain. The findings offer …
Published in International Journal of Advanced Robotics and Automation Technology · Vol. 1, Issue 3, 2023 · pp. 1–15 Read article