The Emergence of Deep Reinforcement Learning for Path Planning
Nguyen, Thanh Thi Nahavandi, Saeid Razzak, Imran Nguyen, Dung Pham, Nhat Truong Nguyen, Quoc Viet Hung
Nguyen, Thanh Thi
Nahavandi, Saeid
Razzak, Imran
Nguyen, Dung
Pham, Nhat Truong
Nguyen, Quoc Viet Hung
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Computational Biology
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Conference proceeding
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Abstract
The increasing demand for autonomous systems in complex and dynamic environments has driven significant research into intelligent path planning methodologies. For decades, graph-based search algorithms, linear programming techniques, and evolutionary computation methods have served as foundational approaches in this domain. Recently, deep reinforcement learning (DRL) has emerged as a powerful method for enabling autonomous agents to learn optimal navigation strategies through interaction with their environments. This survey provides a comprehensive overview of traditional approaches as well as the recent advancements in DRL applied to path planning tasks, focusing on autonomous vehicles, drones, and robotic platforms. Key algorithms across both conventional and learning-based paradigms are categorized, with their innovations and practical implementations highlighted. This is followed by a thorough discussion of their respective strengths and limitations in terms of computational efficiency, scalability, adaptability, and robustness. The survey concludes by identifying key open challenges and outlining promising avenues for future research. Special attention is given to hybrid approaches that integrate DRL with classical planning techniques to leverage the benefits of both learning-based adaptability and deterministic reliability, offering promising directions for robust and resilient autonomous navigation.
Citation
T.T. Nguyen, S. Nahavandi, I. Razzak, D. Nguyen, N.T. Pham, Q.V.H. Nguyen, "The Emergence of Deep Reinforcement Learning for Path Planning," 2026, pp. 6265-6272.
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Conference
2025 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
Keywords
46 Information and Computing Sciences, 4602 Artificial Intelligence, 4605 Data Management and Data Science
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Source
2025 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
Publisher
IEEE