Open Access
ARTICLE
Reinforcement Learning Algorithms for Autonomous Robotic Navigation in Dynamic Environments
Issue Vol. 1 No. 01 (2025): Volume 01 , Issue 01 Oct 2025 --- Section Articles --- Published Date: 2025-10-01
Abstract
Autonomous robotic navigation has become a core requirement in modern industrial, commercial, and defense operations, where robots must operate in environments characterized by uncertainty, dynamic obstacles, limited sensing, and continuous adaptation. This research investigates the application of Reinforcement Learning (RL) algorithms—namely Deep Q-Networks (DQN), Proximal Policy Optimization (PPO), Soft Actor-Critic (SAC), and Multi-Agent RL—in enabling robots to learn optimal navigation strategies through trial-and-error interactions with their surroundings. The study evaluates how RL facilitates real-time decision-making, obstacle avoidance, path optimization, energy-efficient movement, and robust autonomy. Experimental simulation results and comparative analysis demonstrate that advanced RL algorithms significantly enhance robot responsiveness, prediction accuracy, and long-term performance in highly dynamic environments. The findings position RL as a critical enabler for next-generation autonomous systems in manufacturing, logistics, surveillance, precision farming, and disaster management.
Article Cover Image
