Quantum Computing and Neuromorphic Computing for Safe, Reliable, and explainable Multi-Agent Reinforcement Learning: Optimal Control in Autonomous Robotics
<|reference_start|>Quantum Computing and Neuromorphic Computing for Safe, Reliable, and explainable Multi-Agent Reinforcement Learning: Optimal Control in Autonomous Robotics: This paper investigates the utilization of Quantum Computing and Neuromorphic Computing for Safe, Reliable, and Explainable Multi_Agent Reinforcement Learning (MARL) in the context of optimal control in autonomous robotics. The objective was to address the challenges of optimizing the behavior of autonomous agents while ensuring safety, reliability, and explainability. Quantum Computing techniques, including Quantum Approximate Optimization Algorithm (QAOA), were employed to efficiently explore large solution spaces and find approximate solutions to complex MARL problems. Neuromorphic Computing, inspired by the architecture of the human brain, provided parallel and distributed processing capabilities, which were leveraged to develop intelligent and adaptive systems. The combination of these technologies held the potential to enhance the safety, reliability, and explainability of MARL in autonomous robotics. This research contributed to the advancement of autonomous robotics by exploring cutting-edge technologies and their applications in multi-agent systems. Codes and data are available.<|reference_end|>
@article{taghavi2024quantum,
    title={Quantum Computing and Neuromorphic Computing for Safe, Reliable, and
  explainable Multi-Agent Reinforcement Learning: Optimal Control in Autonomous
  Robotics},
    author={Mazyar Taghavi},
    journal={arXiv preprint arXiv:2408.03884},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.03884},
    primaryClass={cs.ET cs.LG cs.MA}
}