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@article{182046,
        author = {Mr. Rahul Singh and Prof. Satish Kumbhar},
        title = {The Performance Evaluation of Reinforcement Learning Algorithms for Autonomous Navigation in Simulated Environments},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {2},
        pages = {725-732},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=182046},
        abstract = {This study presents an exploration into the use of Reinforcement Learning (RL), specifically Deep Q-Networks (DQN), for autonomous drone navigation within complex, obstacle-rich environments. Utilizing Microsoft’s AirSim simulator and an open-source DRL integration framework (AirsimDRL), the research trains a drone to intelligently reach target destinations while avoiding collisions. The agent interacts with a dynamic simulated world, learning optimal control strategies from scratch. The study aims to bridge the gap between traditional UAV path planning and intelligent, learning-based navigation systems, laying the foundation for real-world autonomous drone applications.},
        keywords = {Reinforcement Learning (RL), Deep Q-Network (DQN), Autonomous Drone Navigation, AirSim Simulator, UAV, Deep Reinforcement Learning (DRL), Obstacle Avoidance, Smart Mobility, AI-based Navigation, Flight Path Optimization.},
        month = {July},
        }