Quantum-Enhanced Hybrid Reinforcement Learning Framework for Dynamic Path Planning in Autonomous Systems (2504.20660v2)

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.

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

The paper presents a hybrid quantum-classical framework designed to enhance reinforcement learning (RL) capabilities for dynamic path planning in autonomous systems. This framework synergizes quantum and classical RL methods to address obstacles such as static, dynamic, and moving entities in real-time navigation scenarios.

The authors propose leveraging quantum computing's inherent parallelism to generate robust Q-tables and efficiently estimate turn costs, significantly accelerating the convergence of RL training compared to purely classical approaches. This expedites response times and improves the system's adaptability to changing environments, a necessity for modern applications like unmanned aerial vehicles (UAVs), drones, and autonomous vehicles operating in unpredictable urban landscapes.

Methodology

1. Quantum-Assisted Learning: The framework integrates quantum action-value estimation through Q-learning and turn-cost estimations via Quantum Density Turn Learning (Q*). A novel use of quantum circuits facilitates rapid updates to Q-tables, efficiently computing dense turn costs that traditional RL might struggle with.
2. Hybrid Approach: By incorporating quantum-derived data into classical RL pipelines, the model refines state-action policies with increased speed and accuracy, reflecting quantum computing's capacity to explore multiple state spaces simultaneously.
3. Dynamic Adjustments: Real-world simulations, including IIT Delhi campus data, validate the framework's capacity to handle diverse scenarios and dynamic replanning, ensuring autonomous systems can navigate effectively when encountering unpredicted obstacles.

Experimental Evaluation and Results

The experimental setup evaluated the hybrid model in varied environments, demonstrating its superior performance relative to classical algorithms, such as A*. Key metrics included:

• Training Time: The quantum-enhanced model showed a marked reduction in training duration—from days in classical settings to mere minutes, emphasizing its practical applicability for real-time dynamic path adjustments.
• Path Efficiency: The system maintained a mission success rate of 99%, significantly reducing path deviation and unnecessary turns, even with frequent replanning needs. Notably, the hybrid approach reduced path lengths by 15-20% compared to classical methods.
• Success Rate and Replanning Frequency: The robust quantum-enhanced policy facilitated greater flexibility and adaptability in dynamic obstacle-rich environments, resulting in fewer mission failures.

The authors meticulously highlight the potential quantum computing brings to the simulation, optimization, and real-time adaptability challenges posed by dynamic path planning. Responding to the limitations of RL systems in addressing rapidly evolving situations, the quantum-classical hybrid framework constitutes a considerable step forward.

Implications and Future Directions

Practically, this framework could revolutionize autonomous system navigation, optimizing the trajectory planning process in densely populated urban spaces and enhancing mission success in sectors such as drone logistics, autonomous vehicles, and search-and-rescue operations. Future exploration could involve actual quantum hardware implementations aligning with advances in qubits and quantum devices, further refining the quantum-classical synergy demonstrated herein. Integrating this framework into multi-agent systems, like coordinated fleets of autonomous drones, would be an anticipated development, enhancing collaborative strategies and dynamic decision-making.

The paper represents a significant contribution to the ongoing evolution of navigation technology through advanced computational methods, poised to reshape strategies across autonomous systems industries.