mlsanityData to 7 Oct 2026

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

arXiv 2408.03884 ↗First posted 29 Jul 2024cs.ET cs.LG cs.MAcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 9 of its 51 references that are in the map. First posted on arXiv 29 Jul 2024.

Builds on

9 of 51 references.

References · 9

  1. 2017Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environmentscited by 1,519
  2. 2017Constrained Policy Optimizationcited by 928
  3. 2018QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learningcited by 697
  4. 2021Supervised quantum machine learning models are kernel methodscited by 199
  5. 2018Programmatically Interpretable Reinforcement Learningcited by 192
  6. 2017Visualizing and Understanding Atari Agentscited by 141
  7. 2015Memory and information processing in neuromorphic systemscited by 47
  8. 2019Learning Causal State Representations of Partially Observable Environmentscited by 30
  9. 2021Introduction to Quantum Reinforcement Learning: Theory and PennyLane-based Implementationcited by 23

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Citing papers per year: 2025: 2.

2 papers.

  1. 2025Latent Variable Modeling in Multi-Agent Reinforcement Learning via Expectation-Maximization for UAV-Based Wildlife Protectioncited by 1
  2. 2025Quantum-Inspired Multi Agent Reinforcement Learning for Exploration Exploitation Optimization in UAV-Assisted 6G Network Deploymentcited by 0