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International Journal of Advanced Technology and Engineering Exploration

Quantum prioritized experience replay with MaDi-based priority and quantum circuit mechanisms for optimizing reinforcement learning

Published Dec 31, 2024• 0 citations
Publisher

Abstract

Reinforcement learning (RL) encounters significant challenges related to scalability and computational efficiency, particularly in complex decision-making environments. Traditional RL algorithms often struggle with large-scale tasks, necessitating innovative approaches to enhance adaptability and performance. The quantum circuit-based priority replay (QCPR) algorithm was introduced in this study, leveraging quantum-inspired techniques to enhance learning efficiency and decision-making quality through quantum computing (QC). The QCPR algorithm implements two key mechanisms for prioritizing experiences: the magnitude and direction (MaDi) based priority, which assesses the significance and directionality of experiences, and QCPR-specific methods that ensure efficient experience replay. These mechanisms are seamlessly integrated into the Q-learning (QL) framework to optimize the RL process for superior performance. QCPR was evaluated against four well-established RL algorithms-QL, deep Q-networks (DQN), prioritized experience replay (PER), and dueling DQN (DDQN)-using standard decision-making benchmarks. The algorithm was further tested in various simulation and real-world environments, including Atari games, Qiskit integration, and Raspberry Pi hardware and software setups. These evaluations demonstrated QCPR’s adaptability and robustness, showcasing its capability for dynamic, large-scale applications. The results revealed that QCPR significantly outperforms its counterparts across key performance metrics, including accuracy, precision, recall, F1-score, and cumulative rewards. Specifically, QCPR achieved a 15.29% improvement in accuracy over QL, a 9.98% increase over DQN, a 6.33% improvement over PER, and an 8.23% enhancement over DDQN. This study highlights the potential of quantum-inspired approaches to advance RL, offering scalable and efficient solutions for complex decision-making tasks.

DOI
10.19101/ijatee.2024.111100094

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Reinforcement learning (RL) encounters significant challenges related to scalability and computational efficiency, particularly in complex decision-making environments. Traditional RL algorithms often struggle with large-scale tasks, necessitating innovative approaches to enhance adaptability and performance. The quantum circuit-based priority replay (QCPR) algorithm was introduced in this study, leveraging quantum-inspired techniques to enhance learning efficiency and decision-making quality through quantum computing (QC). The QCPR algorithm implements two key mechanisms for prioritizing experiences: the magnitude and direction (MaDi) based priority, which assesses the significance and directionality of experiences, and QCPR-specific methods that ensure efficient experience replay. These mechanisms are seamlessly integrated into the Q-learning (QL) framework to optimize the RL process for superior performance. QCPR was evaluated against four well-established RL algorithms-QL, deep Q-networks (DQN), prioritized experience replay (PER), and dueling DQN (DDQN)-using standard decision-making benchmarks. The algorithm was further tested in various simulation and real-world environments, including Atari games, Qiskit integration, and Raspberry Pi hardware and software setups. These evaluations demonstrated QCPR’s adaptability and robustness, showcasing its capability for dynamic, large-scale applications. The results revealed that QCPR significantly outperforms its counterparts across key performance metrics, including accuracy, precision, recall, F1-score, and cumulative rewards. Specifically, QCPR achieved a 15.29% improvement in accuracy over QL, a 9.98% increase over DQN, a 6.33% improvement over PER, and an 8.23% enhancement over DDQN. This study highlights the potential of quantum-inspired approaches to advance RL, offering scalable and efficient solutions for complex decision-making tasks.
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In this article, a novel training paradigm inspired by quantum computation is proposed for deep reinforcement learning (DRL) with experience replay. In contrast to the traditional experience replay mechanism in DRL, the proposed DRL with quantum-inspired experience replay (DRL-QER) adaptively chooses experiences from the replay buffer according to the complexity and the replayed times of each experience (also called transition), to achieve a balance between exploration and exploitation. In DRL-QER, transitions are first formulated in quantum representations and then the preparation operation and depreciation operation are performed on the transitions. In this process, the preparation operation reflects the relationship between the temporal-difference errors (TD-errors) and the importance of the experiences, while the depreciation operation is taken into account to ensure the diversity of the transitions. The experimental results on Atari 2600 games show that DRL-QER outperforms state-of-the-art algorithms, such as DRL-PER and DCRL on most of these games with improved training efficiency and is also applicable to such memory-based DRL approaches as double network and dueling network.
Exploring the convergence of quantum computing and machine learning, this paper delves into Quantum Reinforcement Learning (QRL) with a specific focus on Variational Quantum Circuits (VQC). Alongside a comprehensive examination of the field, the study presents a concrete implementation of QRL tailored to foundational gym environments. Leveraging principles from Quantum Computing, experiments reveal QRL’s potential in enhancing reinforcement learning tasks within these environments, showcasing notable space optimization for RL models. Quantitative results indicate a reduction in the number of trainable parameters by up to 90% when compared to classical approaches, significantly improving efficiency, mainly due to quantum principles of superposition and entanglement. This highlights the promising implications of integrating quantum computing techniques to address challenges and advance capabilities in fundamental reinforcement learning scenarios. Furthermore, the study discusses the adaptability of QRL algorithms to diverse problem domains, suggesting their potential for scalability and applicability beyond simple environments. Such versatility underscores the robustness and practical relevance of QRL methodologies in real-world scenarios, positioning them as valuable tools for tackling complex reinforcement learning challenges.
Abstract This paper introduces the parallel quantum transfer fractal priority reply with dynamic memory (P‐QTFPR‐DM) algorithm, an innovative approach that combines quantum computing and reinforcement learning (RL) to enhance decision‐making in autonomous vehicles. Leveraging quantum principles such as superposition and entanglement, P‐QTFPR‐DM optimises Q ‐value approximation through a custom quantum circuit (UQC), facilitating efficient exploration and exploitation in high‐dimensional state‐action spaces. This algorithm utilises a quantum neural network (QNN) with 4 qubits to encode and process Q ‐values. The autonomous vehicle, equipped with GPS for real‐time navigation, uses P‐QTFPR‐DM to reach a predefined destination with coordinates 12.82,514,234,148 latitude and 80.0,451,311,962,242 longitude. Through extensive numerical simulations, P‐QTFPR‐DM demonstrates a 30% reduction in decision‐making time and a 25% improvement in navigation accuracy compared to classical RL methods. The QNN‐based approach achieves a 95% success rate in reaching the destination within a 5‐m accuracy threshold, whereas traditional RL methods achieve only an 85% success rate. Dynamic memory management in P‐QTFPR‐DM optimises computational resources, enhancing the vehicle's adaptability to environmental changes. These results highlight the potential of quantum computing to significantly advance autonomous vehicle technology by improving both efficiency and effectiveness in complex navigation tasks. Future research will focus on refining the algorithm and exploring its real‐world applications to enhance autonomous vehicle performance.
This paper explores the application of Quantum-Enhanced Reinforcement Learning (QRL) to robotics, aiming to accelerate learning speeds and enable more intelligent decision-making in complex environments. Traditional reinforcement learning algorithms often struggle with scalability and convergence, particularly when dealing with high-dimensional state spaces and complex environments. Quantum computing offers the potential to overcome these limitations by leveraging quantum mechanics to perform computations that are intractable for classical computers. We propose a novel QRL framework that utilizes quantum-enhanced reinforcement learning to improve exploration, adaptation, and overall performance. The core mechanism involves employing quantum circuits to represent and manipulate the state space, facilitating more efficient learning and potentially uncovering novel strategies. This research investigates the feasibility and potential impact of QRL on robotic control, focusing on scenarios requiring rapid adaptation and robust decision-making in dynamic environments. We present preliminary results demonstrating the efficacy of our approach in a simulated robotic task involving navigation and obstacle avoidance.
This paper introduces the QDQN-DPER framework to enhance the efficiency of quantum reinforcement learning (QRL) in solving sequential decision tasks. The framework incorporates prioritized experience replay, asynchronous training and novel matrix loss into the training algorithm to reduce the high sampling complexities. Numerical simulations demonstrate that QDQN-DPER outperforms the baseline distributed quantum Q-learning with the same model architecture. The proposed framework holds potential for more complex tasks while maintaining training efficiency.