Quantum-Enhanced Hybrid Reinforcement Learning Framework for Dynamic Path Planning in Autonomous Systems
This addresses path planning for autonomous systems in complex environments, representing an incremental advance by integrating quantum computing with classical methods.
The paper tackles dynamic path planning for autonomous systems by proposing a quantum-classical hybrid reinforcement learning framework that reduces training time and improves adaptability to obstacles. Simulator evaluations showed enhancements in path efficiency, trajectory smoothness, and mission success rates, with testing on real-world data like the IIT Delhi campus.
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.