Abstract Quantum reinforcement learning (QRL) can change computational paradigms through quantum parallelism. A novel algorithm called Quantum Task Impact Learning (QTIL) is coined in this paper, specially designed for the Cart-Pole environment. QTIL combines neural networks with quantum circuits to improve decision-making in dynamic environments. By manipulating quantum states iteratively (ψ) via a policy circuit (P) and computing temporal differences (δ) to update priorities depending on task importance, efficient adaptation to Cart-Pole dynamics is ensured by QTIL. Its prioritized experience replays mechanism guides quantum circuit parameter updates across minibatches thus enabling focused learning. As such, convergence can be achieved faster, and performance enhanced due to the role played by quantum states in Cart-Pole reinforcement learning. The fusion of quantum and artificial intelligence methods into QTIL opens possibilities for advances in quantum-enhanced machine learning especially in complicated issues like Cart-Pole. Potential exploration and optimization of QTIL may greatly enhance synergy between these two quantum computing paradigms leading to breakthroughs in reinforcement learning that are nothing short of transformative.
Abstract Quantum reinforcement learning (QRL) can change computational paradigms through quantum parallelism. A novel algorithm called Quantum Task Impact Learning (QTIL) is coined in this paper, specially designed for the Cart-Pole environment. QTIL combines neural networks with quantum circuits to improve decision-making in dynamic environments. By manipulating quantum states iteratively (ψ) via a policy circuit (P) and computing temporal differences (δ) to update priorities depending on task importance, efficient adaptation to Cart-Pole dynamics is ensured by QTIL. Its prioritized experience replays mechanism guides quantum circuit parameter updates across minibatches thus enabling focused learning. As such, convergence can be achieved faster, and performance enhanced due to the role played by quantum states in Cart-Pole reinforcement learning. The fusion of quantum and artificial intelligence methods into QTIL opens possibilities for advances in quantum-enhanced machine learning especially in complicated issues like Cart-Pole. Potential exploration and optimization of QTIL may greatly enhance synergy between these two quantum computing paradigms leading to breakthroughs in reinforcement learning that are nothing short of transformative.
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.
The key approaches for machine learning, particularly learning in unknown probabilistic environments, are new representations and computation mechanisms. In this paper, a novel quantum reinforcement learning (QRL) method is proposed by combining quantum theory and reinforcement learning (RL). Inspired by the state superposition principle and quantum parallelism, a framework of a value-updating algorithm is introduced. The state (action) in traditional RL is identified as the eigen state (eigen action) in QRL. The state (action) set can be represented with a quantum superposition state, and the eigen state (eigen action) can be obtained by randomly observing the simulated quantum state according to the collapse postulate of quantum measurement. The probability of the eigen action is determined by the probability amplitude, which is updated in parallel according to rewards. Some related characteristics of QRL such as convergence, optimality, and balancing between exploration and exploitation are also analyzed, which shows that this approach makes a good tradeoff between exploration and exploitation using the probability amplitude and can speedup learning through the quantum parallelism. To evaluate the performance and practicability of QRL, several simulated experiments are given, and the results demonstrate the effectiveness and superiority of the QRL algorithm for some complex problems. This paper is also an effective exploration on the application of quantum computation to artificial intelligence.
Quantum Machine Learning (QML) represents a paradigm shift in machine learning, leveraging quantum mechanical principles to accelerate computation and potentially unlock new algorithms. This paper investigates a novel reinforcement learning (RL) framework, termed "Adaptive Neural Network Reinforcement Learning" (ANNR), which employs dynamic parameter adjustment within a self-adaptive neural network to enhance learning efficiency and generalization capabilities. We demonstrate that this approach overcomes limitations of traditional RL methods, offering a promising path towards more robust and adaptable reinforcement learning agents. The core mechanism focuses on iteratively refining neural network weights through a feedback loop, enabling the system to explore complex environments effectively. The research explores the impact of parameter adjustment on both training speed and final performance across various RL tasks.
Quantum Reinforcement Learning (QRL) represents a novel approach to reinforcement learning that leverages the unique capabilities of quantum computing to enhance learning efficiency and enable the exploration of complex behavioral rules. This paper explores the theoretical foundations, design principles, and potential applications of a QRL algorithm, focusing on its ability to learn intricate patterns and make superior decisions within challenging environments. The core mechanism centers around quantum superposition and entanglement, strategically employed to represent the state space and guide the agent's exploration, resulting in improved learning speed and robustness. We present a detailed framework for implementing this algorithm, discussing its potential impact on diverse reinforcement learning scenarios, particularly those encountered in edge applications. The paper concludes with a discussion of ongoing research directions and future prospects for QRL.
Recent advances in quantum computing (QC) and machine learning (ML) have drawn significant attention to the development of quantum machine learning (QML). Reinforcement learning (RL) is one of the ML paradigms which can be used to solve complex sequential decision making problems. Classical RL has been shown to be capable to solve various challenging tasks. However, RL algorithms in the quantum world are still in their infancy. One of the challenges yet to solve is how to train quantum RL in the partially observable environments. In this paper, we approach this challenge through building QRL agents with quantum recurrent neural networks (QRNN). Specifically, we choose the quantum long short-term memory (QLSTM) to be the core of the QRL agent and train the whole model with deep Q-learning. We demonstrate the results via numerical simulations that the QLSTM-DRQN can solve standard benchmark such as Cart-Pole with more stable and higher average scores than classical DRQN with similar architecture and number of model parameters.
Quantum Reinforcement Learning (QRL) represents a groundbreaking approach at the intersection of quantum computing and artificial intelligence, promising to revolutionize decision-making processes across various domains. This chapter explores the fundamental principles of QRL and its diverse applications in critical sectors, including transportation networks, healthcare, financial decision-making, and smart energy systems. By leveraging the computational advantages of quantum algorithms, QRL enhances traditional reinforcement learning techniques, enabling more efficient data processing and improved strategic decision-making capabilities. Case studies within this chapter illustrate the transformative potential of QRL in optimizing logistics, improving medical imaging, and enhancing energy management strategies. The findings underscore the need for further exploration of quantum applications to fully harness their capabilities and address contemporary challenges. This comprehensive examination provides a solid foundation for researchers and practitioners aiming to integrate quantum technologies into real-world applications, paving the way for innovative solutions and sustainable advancements.