Deep reinforcement learning based mobile robot navigation: A review

Zhu, Kai; Zhang, Tao

doi:10.26599/tst.2021.9010012

Cited by 256 publications

(121 citation statements)

References 69 publications

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“…Huang et al 22 established an MMBE model to discover the user's check-in behaviors. Recently, deep learning [23][24][25] and RNN have been proved to be effective in sequence prediction. Liu et al 26 proposed spatial and temporal RNN (ST-RNN) by combining spatiotemporal information.…”

Section: Next Poi Predictionmentioning

confidence: 99%

Bidirectional GRU networks‐based next POI category prediction for healthcare

Liu

Song

et al. 2021

Int J of Intelligent Sys

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The Corona Virus Disease 2019 has a great impact on public health and public psychology. People stay at home for a long time and rarely go out. With the improvement of the epidemic situation, people began to go to different places to check in. To maintain public mental health, it is necessary to propose a point‐of‐interest (POI) prediction model which can mine users' interests. However, the current techniques suffer from lower precision during prediction and the practical value is poor, which is due to the sparse data of users' check‐in. Faced with this challenge, we propose an attention‐based bidirectional gated recurrent unit (GRU) model for POI category prediction (ABG_poic). We regard the user's POI category as the user's interest preference because the fuzzy POI category is easier to reflect the user's interest than the POI. This method can alleviate the data sparsity, and protect users' location privacy. Since users' preferences are variable, we utilize a bidirectional GRU to capture the dynamic dependence of users' check‐ins. Furthermore, since the neural network is similar to a “black box” in feature learning, the decision‐making stage is opaque. Thus, we combine the attention mechanism with bidirectional GRU to selectively focus on historical check‐in records, which can improve the interpretability of the model. Considering the time impact on users' check‐in, we utilize the time sliding window in the ABG_poic model. Experiments on two data sets demonstrate that our ABG_poic outperforms the comparison models for POI category prediction on sparse check‐in data.

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Section: Next Poi Predictionmentioning

confidence: 99%

Bidirectional GRU networks‐based next POI category prediction for healthcare

Liu

Song

et al. 2021

Int J of Intelligent Sys

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“…Reinforcement learning is a machine learning scheme involved in training an action policy to maximize the total reward in a particular situation or environment [5]. Various applications have been studied for reinforcement learning, such as autonomous driving vehicles [6], robot control [7], communication security [8], and elastic optical networks [9]. Recently, many algorithms used for reinforcement learning have been actively developed.…”

Section: Introductionmentioning

confidence: 99%

Photonic reinforcement learning based on optoelectronic reservoir computing

Kanno

Uchida

2022

Sci Rep

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Reinforcement learning has been intensively investigated and developed in artificial intelligence in the absence of training data, such as autonomous driving vehicles, robot control, internet advertising, and elastic optical networks. However, the computational cost of reinforcement learning with deep neural networks is extremely high and reducing the learning cost is a challenging issue. We propose a photonic on-line implementation of reinforcement learning using optoelectronic delay-based reservoir computing, both experimentally and numerically. In the proposed scheme, we accelerate reinforcement learning at a rate of several megahertz because there is no required learning process for the internal connection weights in reservoir computing. We perform two benchmark tasks, CartPole-v0 and MountanCar-v0 tasks, to evaluate the proposed scheme. Our results represent the first hardware implementation of reinforcement learning based on photonic reservoir computing and pave the way for fast and efficient reinforcement learning as a novel photonic accelerator.

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“…Deep reinforcement learning (DRL), as an important research direction in machine learning [1] , is a combination of the agent perception capability of deep learning and the decision making capability of reinforcement learning, directly through the learning of high-dimensional perceptual inputs [2] to eventually achieve the autonomous behavior control of an agent, which continuously describes the process of an agent for achieving a task. DRL has made breakthroughs in areas such as unmanned vehicles, robotic transportation systems, robotic systems, and games [3] .…”

Section: Introductionmentioning

confidence: 99%

Hybrid Navigation Method for Multiple Robots Facing Dynamic Obstacles

Zhao

2022

Tsinghua Sci. Technol.

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With the continuous development of robotics and artificial intelligence, robots are being increasingly used in various applications. For traditional navigation algorithms, such as Dijkstra and A , many dynamic scenarios in life are difficult to cope with. To solve the navigation problem of complex dynamic scenes, we present an improved reinforcement-learning-based algorithm for local path planning that allows it to perform well even when more dynamic obstacles are present. The method applies the gmapping algorithm as the upper layer input and uses reinforcement learning methods as the output. The algorithm enhances the robots' ability to actively avoid obstacles while retaining the adaptability of traditional methods.

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Deep reinforcement learning based mobile robot navigation: A review

Cited by 256 publications

References 69 publications

Bidirectional GRU networks‐based next POI category prediction for healthcare

Bidirectional GRU networks‐based next POI category prediction for healthcare

Photonic reinforcement learning based on optoelectronic reservoir computing

Hybrid Navigation Method for Multiple Robots Facing Dynamic Obstacles

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