A quantum-inspired Q-learning (QIQL) algorithm is proposed for indoor robot navigation control. Q- learning is an action-dependent reinforcement learning method and has been widely used in robot navigation. Inspired by the fundamental characteristics of quantum computation, e.g. state superposition principle and quantum parallel computation, probability is introduced to Q-learning and along with the learning process the probability of each action to be selected at a certain state is updated, which leads to a natural exploration strategy instead of a pointed one with configured parameters. The simulated navigation experiments show that the proposed QIQL algorithm keeps a good balance of exploration and exploitation, which can avoid the local optimal policies and accelerate the learning process as well.
A quantum-inspired Q-learning (QIQL) algorithm is proposed for indoor robot navigation control. Q- learning is an action-dependent reinforcement learning method and has been widely used in robot navigation. Inspired by the fundamental characteristics of quantum computation, e.g. state superposition principle and quantum parallel computation, probability is introduced to Q-learning and along with the learning process the probability of each action to be selected at a certain state is updated, which leads to a natural exploration strategy instead of a pointed one with configured parameters. The simulated navigation experiments show that the proposed QIQL algorithm keeps a good balance of exploration and exploitation, which can avoid the local optimal policies and accelerate the learning process as well.
A novel quantum-inspired reinforcement learning (QiRL) algorithm is proposed for navigation control of autonomous mobile robots. The QiRL algorithm adopts a probabilistic action selection policy and a new reinforcement strategy, which are inspired, respectively, by the collapse phenomenon in quantum measurement and amplitude amplification in quantum computation. Several simulated experiments of Markovian state transition demonstrate that QiRL is more robust to learning rates and initial states than traditional reinforcement learning. The QiRL approach is then applied to navigation control of a real mobile robot, and the simulated and experimental results show the effectiveness of the proposed approach.
Abstract: Quantum deep reinforcement learning is an integration of the principles of quantum computing with deep reinforcement learning in an effort to advance robot navigation tasks. Due to the explosion of the state and action spaces, traditionally DRL methods are limited in scalability and efficiency. QDRL, however, uses quantum superposition and entanglement for more efficient processing of big amounts of information-it enables robots to learn optimal policies for navigating complex environments. We introduce here a new QDRL framework in which quantum neural networks encode policy functions and value estimates. We demonstrate how quantum circuits can take advantage of multi-dimensional state representations in order to strengthen the power of a robot in changing its environment over time. Benchmark experiments on navigation tasks reflect phenomenal acceleration in learning performance along with better decision-making accuracy compared to classical DRL methods. We then consider integrating such quantum algorithms, as Grover's search, to improve exploration strategies in sparse reward settings. The experiments show that QDRL speeds up convergence rates and provides robots with the possibility to react more rapidly to unexpected obstacles. It discusses new applications for quantum computing of autonomous systems, potentially applied to robotics, autonomous vehicles, and other environments which require complex decision making. Some lines of work in the future are optimization of the resources needed for quantum computers and hybrid architectures for the resolution of classical problems in navigation
This paper investigates the application of quantum-inspired algorithms to the field of reinforcement learning (RL). Traditional RL methods often struggle with exploration challenges and can be computationally expensive, particularly in complex environments. We propose a novel RL algorithm that leverages concepts from quantum mechanics, specifically superposition and entanglement, to address these limitations. The core idea is to represent states, actions, and rewards using quantum-inspired representations, allowing the agent to explore the environment more efficiently and potentially converge to optimal policies faster. This work presents a detailed formulation of the algorithm, including the mathematical framework and key considerations for implementation. The potential benefits of this approach, including improved exploration and reduced computational complexity, are discussed. The algorithm is designed to iteratively refine its policy based on simulated quantum measurements, aiming to minimize the expected return. The exploration strategy is guided by the inherent probabilistic nature of quantum states, leading to a more diverse and potentially faster learning process. Further research is warranted to fully explore the capabilities and limitations of this approach across various RL benchmark problems.
According to the existing problems in RL area, such as low learning speed and tradeoff between exploration and exploitation, SIRL and QRL methods are introduced based on the theory of RL and quantum computation in this chapter, which follows the developing roadmap from the superposition-inspired methods to the RL methods in quantum systems. Just as simulated annealing algorithm comes from mimicking the physical annealing process, quantum characteristics also broaden our mind and provide alternative approaches to novel RL methods. In this chapter, SIRL method emphasizes the exploration policy and uses a probabilistic action selection method that is inspired by the state superposition principle and collapse postulate. The experiments, which include a puzzle problem and a mobile robot navigation problem, demanstrate the effectiveness of SIRL algorithm and show that it is superior to basic TD algorithm with -greedy policy. As for QRL, the state/action value is represented with quantum superposition state and the action selection is carried out by observing quantum state according to quantum collapse postulate, which means a QRL system is designed for the real quantum system although it can also be simulated on a traditional computer. The results of simulated experiments verified its feasibility and effectiveness with two examples: Prisoner's Dilemma and the control of a five-qubit system. The contents presented in this chapter are mainly the basic ideas and methods related to the combination of RL theory and quantum computation. More theoretic research and applictions are to be investigated in the future.
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.
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.