Design and Analysis of Quantum Transfer Fractal Priority Replay with Dynamic Memory (QTFPR-DM) algorithm to Enhancing Robotic Decision-Making by Quantum computing

In the field of robotics, efficient decision-making is essential for navigating complex environments and accomplishing diverse tasks. Quantum Reinforcement Learning (QRL) has emerged as a promising approach to address these challenges, leveraging quantum principles to enhance decision-making processes. However, existing QRL algorithms often face limitations in terms of sample efficiency and convergence speed. To overcome these limitations, we propose a novel algorithm, Quantum Transfer Fractal Priority Replay with Dynamic Memory (QTFPR-DM), designed specifically for robotic decision-making tasks. QTFPR-DM integrates quantum circuits for action selection within a QRL framework. It utilizes a replay buffer to store past experiences and dynamic memory to adaptively update based on experience access frequency. Additionally , a novel priority formula is introduced to prioritize experiences for learning, focusing on those with high learning potential. Experimental results demonstrate the effectiveness of QTFPR-DM in various robotic environments. The algorithm shows significant improvements in convergence speed and sample efficiency compared to conventional approaches. Furthermore, analysis of the exploration-exploitation trade-off reveals how dynamic memory management enhances exploration capabilities while maintaining exploitation efficiency in robotic contexts. QTFPR-DM represents a promising advancement in ro-botic decision-making, offering improved performance and efficiency. By lev-eraging quantum principles alongside dynamic memory and priority replay mechanisms, QTFPR-DM demonstrates its potential to revolutionize decision-making processes in robotics. The insights gained from this study pave the way for the development of more adaptive and efficient robotic systems, with broad implications for real-world applications.