Robust Quantum-Inspired Reinforcement Learning for Robot Navigation

Dong D.; Chen C.; Chu J.; Tarn  T-.J.

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Robust Quantum-Inspired Reinforcement Learning for Robot Navigation

机译：鲁棒的量子启发式强化学习，用于机器人导航

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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.

机译：提出了一种新颖的量子启发式强化学习算法，用于自主移动机器人的导航控制。 QiRL算法采用概率动作选择策略和新的增强策略，分别受量子测量中的崩溃现象和量子计算中的振幅放大的启发。马尔可夫状态转变的几个模拟实验表明，与传统的强化学习相比，QiRL在学习率和初始状态方面更强大。然后将QiRL方法应用于真实移动机器人的导航控制，仿真和实验结果表明了该方法的有效性。

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《Mechatronics, IEEE/ASME Transactions on》 |2012年第1期|p.86-97|共12页
作者
Dong D.; Chen C.; Chu J.; Tarn T-.J.;
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作者单位

Institute of Cyber Systems and Control, State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou, China;

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正文语种 eng
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关键词
Probabilistic action selection; quantum amplitude amplification; quantum-inspired reinforcement learning (QiRL); robot navigation;

机译：概率动作选择;量子幅度放大;量子启发式强化学习（QiRL）;机器人导航;

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专利

1. Behavior acquisition of an autonomous robot by reinforcement learning based on globally coupled chaotic system (2nd report, learning navigational behaviors in dynamic environment) [J] . Yoichiro Nakamura, Kazuhiro Ohkura, Kanji Ueda 日本機械学会論文集. C編 . 1997,第615期

机译：通过基于全局耦合混沌系统的强化学习获得自主机器人的行为（第二份报告，学习动态环境中的导航行为）
2. Improving the Robustness of Instance-Based Reinforcement Learning Robots by Metalearning [J] . Toshiyuki Yasuda, Kousuke Araki, Kazuhiro Ohkura Journal of Advanced Computatioanl Intelligence and Intelligent Informatics . 2011,第8a87期

机译：通过Metalearning提高基于实例的强化学习机器人的鲁棒性
3. Enhanced Autonomous Navigation of Robots by Deep Reinforcement Learning Algorithm with Multistep Method [J] . Xiaohong Peng, Rongfa Chen, Jun Zhang, Sensors and materials . 2021,第2期

机译：利用多步骤方法增强了机器人的自主导航
4. Obtaining Robust Control and Navigation Policies for Multi-robot Navigation via Deep Reinforcement Learning [C] . Christian Jestel, Harmtmut Surmann, Jonas Stenzel, International Conference on Automation, Robotics and Applications . 2021

机译：通过深增强学习获取多机器人导航的强大控制和导航政策
5. Robust Pose Estimation of a Robotic Navigation Aid for the Visually Impaired by Multimodal Data Fusion [D] . Zhang, He. 2018

机译：针对多模态数据融合的视觉障碍者的机器人导航辅助系统的鲁棒姿态估计
6. Learning for a Robot: Deep Reinforcement Learning Imitation Learning Transfer Learning [O] . Jiang Hua, Liangcai Zeng, Gongfa Li, 2021

机译：学习机器人：深增强学习仿制学习转移学习
7. Lifelong Federated Reinforcement Learning: A Learning Architecture for Navigation in Cloud Robotic Systems [O] . Boyi Liu, Lujia Wang, Ming Liu 2019

机译：终身联邦钢筋学习：云机器人系统中导航的学习架构

Robust Quantum-Inspired Reinforcement Learning for Robot Navigation

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