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Brain-Inspired Agents for Quantum Reinforcement Learning

Author

Listed:
  • Eva Andrés

    (Department of Computer Science and Artificial Intelligence, ETSI Informática y de Telecomunicación, Universidad de Granada, C/. Pdta Daniel Saucedo Aranda sn, 18014 Granada, Spain)

  • Manuel Pegalajar Cuéllar

    (Department of Computer Science and Artificial Intelligence, ETSI Informática y de Telecomunicación, Universidad de Granada, C/. Pdta Daniel Saucedo Aranda sn, 18014 Granada, Spain)

  • Gabriel Navarro

    (Department of Computer Science and Artificial Intelligence, ETSI Informática y de Telecomunicación, Universidad de Granada, C/. Pdta Daniel Saucedo Aranda sn, 18014 Granada, Spain)

Abstract

In recent years, advancements in brain science and neuroscience have significantly influenced the field of computer science, particularly in the domain of reinforcement learning (RL). Drawing insights from neurobiology and neuropsychology, researchers have leveraged these findings to develop novel mechanisms for understanding intelligent decision-making processes in the brain. Concurrently, the emergence of quantum computing has opened new frontiers in artificial intelligence, leading to the development of quantum machine learning (QML). This study introduces a novel model that integrates quantum spiking neural networks (QSNN) and quantum long short-term memory (QLSTM) architectures, inspired by the complex workings of the human brain. Specifically designed for reinforcement learning tasks in energy-efficient environments, our approach progresses through two distinct stages mirroring sensory and memory systems. In the initial stage, analogous to the brain’s hypothalamus, low-level information is extracted to emulate sensory data processing patterns. Subsequently, resembling the hippocampus, this information is processed at a higher level, capturing and memorizing correlated patterns. We conducted a comparative analysis of our model against existing quantum models, including quantum neural networks (QNNs), QLSTM, QSNN and their classical counterparts, elucidating its unique contributions. Through empirical results, we demonstrated the effectiveness of utilizing quantum models inspired by the brain, which outperform the classical approaches and other quantum models in optimizing energy use case. Specifically, in terms of average, best and worst total reward, test reward, robustness, and learning curve.

Suggested Citation

  • Eva Andrés & Manuel Pegalajar Cuéllar & Gabriel Navarro, 2024. "Brain-Inspired Agents for Quantum Reinforcement Learning," Mathematics, MDPI, vol. 12(8), pages 1-26, April.
  • Handle: RePEc:gam:jmathe:v:12:y:2024:i:8:p:1230-:d:1378863
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    References listed on IDEAS

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    2. Andrea Banino & Caswell Barry & Benigno Uria & Charles Blundell & Timothy Lillicrap & Piotr Mirowski & Alexander Pritzel & Martin J. Chadwick & Thomas Degris & Joseph Modayil & Greg Wayne & Hubert Soy, 2018. "Vector-based navigation using grid-like representations in artificial agents," Nature, Nature, vol. 557(7705), pages 429-433, May.
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