Quantum-Inspired Optimization for Enhanced Exploration an‎d Exploitation in Multi-Agent Reinforcement Learning: A Case Study in Disease Diagnostics an‎d Treatment by Nanorobotics

10/5/2025 12:00:00 AM

This research investigates the improvement of the exploration an‎d exploitation process in multi-agent reinforcement learning using quantum-inspired optimization. By combining quantum an‎d classical methods, the proposed algorithm can improve the performance of medical nanorobots in the diagnosis an‎d treatment of diseases. The results of this research show that the proposed quantum method is more efficient than classical algorithms an‎d reduces computational costs an‎d increases the convergence speed. These findings can create a new path in the development of advanced artificial intelligence methods an‎d medical applications.

يادگيري تقويتي چند عامله , بهينه سازي كوانتومي , كاوش و بهره برداري , نانو رباتيك

Multi-agent Reinforcement Learning , Quantum Optimization , Exploration an‎d Exploitation , Nanorobotics

Mazyar Taghavi

Javad Vahidi