Article
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Brain-inspired agents for Quantum Reinforcement Learning
Version 1
: Received: 5 March 2024 / Approved: 14 March 2024 / Online: 18 March 2024 (10:24:38 CET)
A peer-reviewed article of this Preprint also exists.
Andrés, E.; Cuéllar, M.P.; Navarro, G. Brain-Inspired Agents for Quantum Reinforcement Learning. Mathematics 2024, 12, 1230. Andrés, E.; Cuéllar, M.P.; Navarro, G. Brain-Inspired Agents for Quantum Reinforcement Learning. Mathematics 2024, 12, 1230.
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 conduct a comparative analysis of our model against existing quantum models such as Quantum Neural Networks and their classical counterparts, elucidating its unique contributions. Through empirical results, we aim to underscore the effectiveness of our approach in optimizing energy consumption in practical scenarios.
Keywords
Quantum Reinforcement Learning; Quantum Neural Networks; Quantum Spiking Neural Network; Quantum Long Short-Term Memory; Brain-inspired models.
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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