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IEEE Transactions on Artificial Intelligence

ReLAQA: Reinforcement Learning-Based Autonomous Quantum Agent for Quantum Applications

Ahmad Alomari, Sathish Kumar

Published Aug 5, 2024• 7 citations
Publisher

Abstract

This study proposes a quantum reinforcement learning (QRL) approach for robotic applications, which incorporates a Grover-based autonomous quantum agent (GAQA) and a quantum environment represented as a quantum TicTacToe (QTTT) game. The QTTT environment is a quantum circuit of qubits in their superposition states, manipulated by the agent through quantum gates to establish a goal state. By utilizing amplitude estimation and Grover search techniques, the proposed reinforcement learning-based autonomous quantum agent (ReLAQA) enhances the probability amplitudes of the actions taken, which results in reducing the number of observed states required to reach a solution. Empirical results substantiate the quantum advantages of the proposed GAQA in reinforcement learning (RL) tasks by observing fewer states of 6300, outperforming classical agents. Therefore, signifying its potential to enhance complex problem-solving in robotics.

DOI
10.1109/tai.2024.3437335

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This study proposes a quantum reinforcement learning (QRL) approach for robotic applications, which incorporates a Grover-based autonomous quantum agent (GAQA) and a quantum environment represented as a quantum TicTacToe (QTTT) game. The QTTT environment is a quantum circuit of qubits in their superposition states, manipulated by the agent through quantum gates to establish a goal state. By utilizing amplitude estimation and Grover search techniques, the proposed reinforcement learning-based autonomous quantum agent (ReLAQA) enhances the probability amplitudes of the actions taken, which results in reducing the number of observed states required to reach a solution. Empirical results substantiate the quantum advantages of the proposed GAQA in reinforcement learning (RL) tasks by observing fewer states of 6300, outperforming classical agents. Therefore, signifying its potential to enhance complex problem-solving in robotics.
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.
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
Combining quantum computing techniques in the form of amplitude amplification with classical reinforcement learning has led to the so-called “hybrid agent for quantum-accessible reinforcement learning”, which achieves a quadratic speedup in sample complexity for certain learning problems. So far, this hybrid agent has only been applied to stationary learning problems, that is, learning problems without any time dependency within components of the Markov decision process. In this work, we investigate the applicability of the hybrid agent to dynamic RL environments and thus to more realistic scenarios. To this end, we enhance the hybrid agent by introducing a dissipation mechanism and, with the resulting learning agent, perform an empirical comparison with a classical RL agent in an RL environment with a time-dependent reward function. Our findings suggest that the modified hybrid agent can adapt its behavior to changes in the environment quickly, leading to a higher average success probability compared to its classical counterpart.
ABSTRACT-This research work focuses on the application of Quantum Reinforcement Learning (QRL) to improve path planning for autonomous robots. Traditional path planning techniques are prone to failure in handling the complexity of uncertain and dynamic environments, but QRL leverages the power of quantum computing to exploit the principles of superposition and parallelism to improve decision-making and optimization processes. By integrating QRL with existing reinforcement learning techniques, the proposed technique quickens the pace of learning processes and improves the quality of path planning to enable robots to navigate around obstacles more effectively with shorter travel time and lower energy costs. Simulation results show that QRL outperforms traditional techniques in convergence speed and optimality of path solutions and offers a promising solution for practical autonomous navigation. Keywords: Dynamic Environment, Path Planning, Autonomous Navigation, Obstacles avoidance.
Quantum reinforcement learning (QRL) can outperform classical reinforcement learning (RL) by utilizing quantum parallel theory and quantum phenomena such as superposition and entanglement. Although some excellent work has been done on QRL, most existing works either fail to show the exponential advantage of quantum computation over classical computation in terms of performance or are too demanding on quantum devices. In this paper, we provide a novel perspective on combining quantum computing and RL with faster convergence speed and relatively relaxed demands on quantum devices. Specifically, we propose a method to construct a world model with quantum circuit that allows it to interact in a quantum way. In addition, we use Grover's algorithm to efficiently extract high-value information from the quantum world model. Extensive simulation results show that the proposed method can have superior performance compared to classical RL algorithms.
Reinforcement learning is a machine learning paradigm where an agent learns how to optimize its behavior solely through its interaction with the environment. It has been extensively studied and successfully applied to complex problems of many different domains in the past decades, i.e., robotics, games, scheduling. However, the performance of these algorithms becomes limited as the complexity and dimension of the state-action space increases. Recent advances in quantum computing and quantum information have sparked interest in possible applications to machine learning. By taking advantage of quantum mechanics, it is possible to efficiently process immense quantities of information and improve computational speed. In this work, we combined quantum computing with reinforcement learning and studied its application to a board game to assess the benefits that it can introduce, namely its impact on the learning efficiency of an agent. From the results, we concluded that the proposed quantum exploration policy improved the convergence rate of the agent and promoted a more efficient exploration of the state space.