Quantum Computing and Neuromorphic Computing for Safe, Reliable, and explainable Multi-Agent Reinforcement Learning: Optimal Control in Autonomous Robotics

TL;DR

This paper explores how Quantum and Neuromorphic Computing can improve the safety, reliability, and explainability of multi-agent reinforcement learning in autonomous robotics, using advanced algorithms and architectures.

Contribution

It introduces the integration of Quantum Approximate Optimization Algorithm and neuromorphic architectures to enhance MARL in autonomous robotics, emphasizing safety and explainability.

Findings

Quantum algorithms efficiently explore large solution spaces.

Neuromorphic computing enables adaptive, parallel processing.

Enhanced safety and explainability in autonomous systems.

Abstract

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.