Quantum-Enhanced Hybrid Reinforcement Learning Framework for Dynamic Path Planning in Autonomous Systems

• URL: http://arxiv.org/abs/2504.20660v2
• Date: Tue, 20 May 2025 06:19:08 GMT
• Title: Quantum-Enhanced Hybrid Reinforcement Learning Framework for Dynamic Path Planning in Autonomous Systems
• Authors: Sahil Tomar, Shamshe Alam, Sandeep Kumar, Amit Mathur,
• Abstract summary: A novel quantum classical hybrid framework is proposed that synergizes quantum with Classical Reinforcement Learning.<n>The proposed approach generates robust Q tables and specialized turn cost estimations, which are then integrated with a classical Reinforcement Learning pipeline.
• Score: 2.7173738939072796
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In this paper, a novel quantum classical hybrid framework is proposed that synergizes quantum with Classical Reinforcement Learning. By leveraging the inherent parallelism of quantum computing, the proposed approach generates robust Q tables and specialized turn cost estimations, which are then integrated with a classical Reinforcement Learning pipeline. The Classical Quantum fusion results in rapid convergence of training, reducing the training time significantly and improved adaptability in scenarios featuring static, dynamic, and moving obstacles. Simulator based evaluations demonstrate significant enhancements in path efficiency, trajectory smoothness, and mission success rates, underscoring the potential of framework for real time, autonomous navigation in complex and unpredictable environments. Furthermore, the proposed framework was tested beyond simulations on practical scenarios, including real world map data such as the IIT Delhi campus, reinforcing its potential for real time, autonomous navigation in complex and unpredictable environments.

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