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arXiv:2202.12180 · IEEE Access

Quantum Deep Reinforcement Learning for Robot Navigation Tasks

Hans Hohenfeld, Dirk Heimann, Felix Wiebe, Frank Kirchner

Published Jan 1, 2024• 43 citations
arXivHTMLPDF

Abstract

We utilize hybrid quantum deep reinforcement learning to learn navigation tasks for a simple, wheeled robot in simulated environments of increasing complexity. For this, we train parameterized quantum circuits (PQCs) with two different encoding strategies in a hybrid quantum-classical setup as well as a classical neural network baseline with the double deep Q network (DDQN) reinforcement learning algorithm. Quantum deep reinforcement learning (QDRL) has previously been studied in several relatively simple benchmark environments, mainly from the OpenAI gym suite. However, scaling behavior and applicability of QDRL to more demanding tasks closer to real-world problems e.g., from the robotics domain, have not been studied previously. Here, we show that quantum circuits in hybrid quantum-classic reinforcement learning setups are capable of learning optimal policies in multiple robotic navigation scenarios with notably fewer trainable parameters compared to a classical baseline. Across a large number of experimental configurations, we find that the employed quantum circuits outperform the classical neural network baselines when equating for the number of trainable parameters. Yet, the classical neural network consistently showed better results concerning training times and stability, with at least one order of magnitude of trainable parameters more than the best-performing quantum circuits. However, validating the robustness of the learning methods in a large and dynamic environment, we find that the classical baseline produces more stable and better performing policies overall. For the two encoding schemes, we observed better results for consecutively encoding the classical state vector on each qubit compared to encoding each component on a separate qubit. Our findings demonstrate that current hybrid quantum machine-learning approaches can be scaled to simple robotic problems while yielding sufficient results, at least in an idealized simulated setting, but there are yet open questions regarding the application to considerably more demanding tasks. We anticipate that our work will contribute to introducing quantum machine learning in general and quantum deep reinforcement learning in particular to more demanding problem domains and emphasize the importance of encoding techniques for classic data in hybrid quantum-classical settings.

DOI
10.1109/access.2024.3417808

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We utilize hybrid quantum deep reinforcement learning to learn navigation tasks for a simple, wheeled robot in simulated environments of increasing complexity. For this, we train parameterized quantum circuits (PQCs) with two different encoding strategies in a hybrid quantum-classical setup as well as a classical neural network baseline with the double deep Q network (DDQN) reinforcement learning algorithm. Quantum deep reinforcement learning (QDRL) has previously been studied in several relatively simple benchmark environments, mainly from the OpenAI gym suite. However, scaling behavior and applicability of QDRL to more demanding tasks closer to real-world problems e.g., from the robotics domain, have not been studied previously. Here, we show that quantum circuits in hybrid quantum-classic reinforcement learning setups are capable of learning optimal policies in multiple robotic navigation scenarios with notably fewer trainable parameters compared to a classical baseline. Across a large number of experimental configurations, we find that the employed quantum circuits outperform the classical neural network baselines when equating for the number of trainable parameters. Yet, the classical neural network consistently showed better results concerning training times and stability, with at least one order of magnitude of trainable parameters more than the best-performing quantum circuits. However, validating the robustness of the learning methods in a large and dynamic environment, we find that the classical baseline produces more stable and better performing policies overall. For the two encoding schemes, we observed better results for consecutively encoding the classical state vector on each qubit compared to encoding each component on a separate qubit. Our findings demonstrate that current hybrid quantum machine-learning approaches can be scaled to simple robotic problems while yielding sufficient results, at least in an idealized simulated setting, but there are yet open questions regarding the application to considerably more demanding tasks. We anticipate that our work will contribute to introducing quantum machine learning in general and quantum deep reinforcement learning in particular to more demanding problem domains and emphasize the importance of encoding techniques for classic data in hybrid quantum-classical settings.
Classical reinforcement learning (RL) methods often struggle in complex, high-dimensional environments because of their extensive parameter requirements and challenges posed by stochastic, non-deterministic settings. This study introduces quantum deep reinforcement learning (QDRL) to train humanoid agents efficiently. While previous quantum RL models focused on smaller environments, such as wheeled robots and robotic arms, our work pioneers the application of QDRL to humanoid robotics, specifically in environments with substantial observation and action spaces, such as MuJoCo's Humanoid-v4 and Walker2d-v4. Using parameterized quantum circuits, we explored a hybrid quantum-classical setup to directly navigate high-dimensional state spaces, bypassing traditional mapping and planning. By integrating quantum computing with deep RL, we aim to develop models that can efficiently learn complex navigation tasks in humanoid robots. We evaluated the performance of the Soft Actor-Critic (SAC) in classical RL against its quantum implementation. The results show that the quantum SAC achieves an 8% higher average return (246.40) than the classical SAC (228.36) after 92% fewer steps, highlighting the accelerated learning potential of quantum computing in RL tasks.
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
One of the multiple facets of quantum reinforcement learning (QRL) is enhancing reinforcement learning (RL) algorithms with quantum submodules, namely with variational quantum circuits (VQC) as function approx-imators. QRL solutions are empirically proven to require fewer training iterations or adjustable parameters than their classical counterparts, but are usually restricted to applications that have a discrete action space and thus limited industrial relevance. We propose a hybrid quantum-classical (HQC) deep deterministic policy gradient (DDPG) approach for a robot to navigate through a maze using continuous states, continuous actions and using local observations from the robot’s LiDAR sensors. We show that this HQC method can lead to models of comparable test results to the neural network (NN)-based DDPG algorithm, that need around 200 times fewer weights. We also study the scalability of our solution with respect to the number of VQC layers and qubits, and find that in general results improve as the layer and qubit counts increase. The best rewards among all similarly sized HQC and classical DDPG methods correspond to a VQC of 8 qubits and 5 layers with no other NN. This work is another step towards continuous QRL, where literature has been sparse.
The development of quantum computational techniques has advanced greatly in recent years, parallel to the advancements in techniques for deep reinforcement learning. This work explores the potential for quantum computing to facilitate reinforcement learning problems. Quantum computing approaches offer important potential improvements in time and space complexity over traditional algorithms because of its ability to exploit the quantum phenomena of superposition and entanglement. Specifically, we investigate the use of quantum variational circuits, a form of quantum machine learning. We present our techniques for encoding classical data for a quantum variational circuit, we further explore pure and hybrid quantum algorithms for DQN and Double DQN. Our results indicate both hybrid and pure quantum variational circuit have the ability to solve reinforcement learning tasks with a smaller parameter space. These comparison are conducted with two OpenAI Gym environments: CartPole and Blackjack, The success of this work is indicative of a strong future relationship between quantum machine learning and deep reinforcement learning.
The development of quantum computational techniques has advanced greatly in recent years, parallel to the advancements in techniques for deep reinforcement learning. This work explores the potential for quantum computing to facilitate reinforcement learning problems. Quantum computing approaches offer important potential improvements in time and space complexity over traditional algorithms because of its ability to exploit the quantum phenomena of superposition and entanglement. Specifically, we investigate the use of quantum variational circuits, a form of quantum machine learning. We present our techniques for encoding classical data for a quantum variational circuit, we further explore pure and hybrid quantum algorithms for DQN and Double DQN. Our results indicate both hybrid and pure quantum variational circuit have the ability to solve reinforcement learning tasks with a smaller parameter space. These comparison are conducted with two OpenAI Gym environments: CartPole and Blackjack, The success of this work is indicative of a strong future relationship between quantum machine learning and deep reinforcement learning.
Reinforcement learning for robot navigation requires representations that capture spatial geometry, long-horizon dependencies, and goal-conditioned structure. Classical neural networks provide flexible function approximators, but their representations are ultimately limited by classical feature transformations. We investigate quantum representation learning as a mechanism for encoding state–goal pairs in goal-conditioned reinforcement learning. We formulate navigation as a finite-horizon Markov decision process and introduce a quantum variational feature map that embeds continuous states into a high-dimensional Hilbert space. This representation is optimized jointly with a Q-learning objective, yielding a hybrid quantum–classical value function. Classical networks serve only as baselines to validate the environment and evaluation protocol. After correcting subtle issues in reward shaping and success detection, we observe that quantum representations produce coherent value structures, stable optimization dynamics, and competitive success rates relative to classical baselines. Rather than aiming to improve navigation performance, our objective is to provide a controlled and reproducible framework for analyzing how quantum feature geometry shapes value estimation and goal-conditioned behavior in offline RL. Our analysis highlights how quantum feature geometry influences value estimation and navigation behavior, establishing a reproducible foundation for studying quantum-enhanced representations in reinforcement learning for robotics.