Models, code, and papers for "Long Yang":

Dual Residual Network for Accurate Human Activity Recognition

Mar 13, 2019
Jun Long, WuQing Sun, Zhan Yang, Osolo Ian Raymond, Bin Li

Human Activity Recognition (HAR) using deep neural network has become a hot topic in human-computer interaction. Machine can effectively identify human naturalistic activities by learning from a large collection of sensor data. Activity recognition is not only an interesting research problem, but also has many real-world practical applications. Based on the success of residual networks in achieving a high level of aesthetic representation of the automatic learning, we propose a novel \textbf{D}ual \textbf{R}esidual \textbf{N}etwork, named DRN. DRN is implemented using two identical path frameworks consisting of (1) a short time window, which is used to capture spatial features, and (2) a long time window, which is used to capture fine temporal features. The long time window path can be made very lightweight by reducing its channel capacity, yet still being able to learn useful temporal representations for activity recognition. In this paper, we mainly focus on proposing a new model to improve the accuracy of HAR. In order to demonstrate the effectiveness of DRN model, we carried out extensive experiments and compared with conventional recognition methods (HC, CBH, CBS) and learning-based methods (AE, MLP, CNN, LSTM, Hybrid, ResNet). The benchmark datasets (OPPORTUNITY, UniMiB-SHAR) were adopted by our experiments. Results from our experiments show that our model is effective in recognizing human activities via wearable datasets. We discuss the influence of networks parameters on performance to provide insights about its optimization.

* submitted to Information 

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Order Matters: Shuffling Sequence Generation for Video Prediction

Jul 20, 2019
Junyan Wang, Bingzhang Hu, Yang Long, Yu Guan

Predicting future frames in natural video sequences is a new challenge that is receiving increasing attention in the computer vision community. However, existing models suffer from severe loss of temporal information when the predicted sequence is long. Compared to previous methods focusing on generating more realistic contents, this paper extensively studies the importance of sequential order information for video generation. A novel Shuffling sEquence gEneration network (SEE-Net) is proposed that can learn to discriminate unnatural sequential orders by shuffling the video frames and comparing them to the real video sequence. Systematic experiments on three datasets with both synthetic and real-world videos manifest the effectiveness of shuffling sequence generation for video prediction in our proposed model and demonstrate state-of-the-art performance by both qualitative and quantitative evaluations. The source code is available at

* This manuscript has been accepted at BMVC 2019. See the project at 

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Forecasting People's Needs in Hurricane Events from Social Network

Nov 12, 2018
Long Nguyen, Zhou Yang, Jia Li, Guofeng Cao, Fang Jin

Social networks can serve as a valuable communication channel for calls for help, offering assistance, and coordinating rescue activities in disaster. Social networks such as Twitter allow users to continuously update relevant information, which is especially useful during a crisis, where the rapidly changing conditions make it crucial to be able to access accurate information promptly. Social media helps those directly affected to inform others of conditions on the ground in real time and thus enables rescue workers to coordinate their efforts more effectively, better meeting the survivors' need. This paper presents a new sequence to sequence based framework for forecasting people's needs during disasters using social media and weather data. It consists of two Long Short-Term Memory (LSTM) models, one of which encodes input sequences of weather information and the other plays as a conditional decoder that decodes the encoded vector and forecasts the survivors' needs. Case studies utilizing data collected during Hurricane Sandy in 2012, Hurricane Harvey and Hurricane Irma in 2017 were analyzed and the results compared with those obtained using a statistical language model n-gram and an LSTM generative model. Our proposed sequence to sequence method forecast people's needs more successfully than either of the other models. This new approach shows great promise for enhancing disaster management activities such as evacuation planning and commodity flow management.

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Policy Optimization with Stochastic Mirror Descent

Jun 25, 2019
Long Yang, Yu Zhang

Stochastic mirror descent (SMD) keeps the advantages of simplicity of implementation, low memory requirement, and low computational complexity. However, the non-convexity of objective function with its non-stationary sampling process is the main bottleneck of applying SMD to reinforcement learning. To address the above problem, we propose the mirror policy optimization (MPO) by estimating the policy gradient via dynamic batch-size of gradient information. Comparing with REINFORCE or VPG, the proposed MPO improves the convergence rate from $\mathcal{O}({{1}/{\sqrt{N}}})$ to $\mathcal{O}({\ln N}/{N})$. We also propose VRMPO algorithm, a variance reduction implementation of MPO. We prove the convergence of VRMPO and show its computational complexity. We evaluate the performance of VRMPO on the MuJoCo continuous control tasks, results show that VRMPO outperforms or matches several state-of-art algorithms DDPG, TRPO, PPO, and TD3.

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Expected Sarsa($λ$) with Control Variate for Variance Reduction

Jun 25, 2019
Long Yang, Yu Zhang

Off-policy learning is powerful for reinforcement learning. However, the high variance of off-policy evaluation is a critical challenge, which causes off-policy learning with function approximation falls into an uncontrolled instability. In this paper, for reducing the variance, we introduce control variate technique to Expected Sarsa($\lambda$) and propose a tabular ES($\lambda$)-CV algorithm. We prove that if a proper estimator of value function reaches, the proposed ES($\lambda$)-CV enjoys a lower variance than Expected Sarsa($\lambda$). Furthermore, to extend ES($\lambda$)-CV to be a convergent algorithm with linear function approximation, we propose the GES($\lambda$) algorithm under the convex-concave saddle-point formulation. We prove that the convergence rate of GES($\lambda$) achieves $\mathcal{O}(1/T)$, which matches or outperforms several state-of-art gradient-based algorithms, but we use a more relaxed step-size. Numerical experiments show that the proposed algorithm is stable and converges faster with lower variance than several state-of-art gradient-based TD learning algorithms: GQ($\lambda$), GTB($\lambda$) and ABQ($\zeta$).

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ActionXPose: A Novel 2D Multi-view Pose-based Algorithm for Real-time Human Action Recognition

Oct 29, 2018
Federico Angelini, Zeyu Fu, Yang Long, Ling Shao, Syed Mohsen Naqvi

We present ActionXPose, a novel 2D pose-based algorithm for posture-level Human Action Recognition (HAR). The proposed approach exploits 2D human poses provided by OpenPose detector from RGB videos. ActionXPose aims to process poses data to be provided to a Long Short-Term Memory Neural Network and to a 1D Convolutional Neural Network, which solve the classification problem. ActionXPose is one of the first algorithms that exploits 2D human poses for HAR. The algorithm has real-time performance and it is robust to camera movings, subject proximity changes, viewpoint changes, subject appearance changes and provide high generalization degree. In fact, extensive simulations show that ActionXPose can be successfully trained using different datasets at once. State-of-the-art performance on popular datasets for posture-related HAR problems (i3DPost, KTH) are provided and results are compared with those obtained by other methods, including the selected ActionXPose baseline. Moreover, we also proposed two novel datasets called MPOSE and ISLD recorded in our Intelligent Sensing Lab, to show ActionXPose generalization performance.

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Towards Reliable, Automated General Movement Assessment for Perinatal Stroke Screening in Infants Using Wearable Accelerometers

Feb 21, 2019
Yan Gao, Yang Long, Yu Guan, Anna Basu, Jessica Baggaley, Thomas Ploetz

Perinatal stroke (PS) is a serious condition that, if undetected and thus untreated, often leads to life-long disability, in particular Cerebral Palsy (CP). In clinical settings, Prechtl's General Movement Assessment (GMA) can be used to classify infant movements using a Gestalt approach, identifying infants at high risk of developing PS. Training and maintenance of assessment skills are essential and expensive for the correct use of GMA, yet many practitioners lack these skills, preventing larger-scale screening and leading to significant risks of missing opportunities for early detection and intervention for affected infants. We present an automated approach to GMA, based on body-worn accelerometers and a novel sensor data analysis method-Discriminative Pattern Discovery (DPD)-that is designed to cope with scenarios where only coarse annotations of data are available for model training. We demonstrate the effectiveness of our approach in a study with 34 newborns (21 typically developing infants and 13 PS infants with abnormal movements). Our method is able to correctly recognise the trials with abnormal movements with at least the accuracy that is required by newly trained human annotators (75%), which is encouraging towards our ultimate goal of an automated PS screening system that can be used population-wide.

* Gao and Long share equal contributions; This work has been accepted for publication in ACM IMWUT (Ubicomp) 2019; 

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Predicting Opioid Relapse Using Social Media Data

Nov 14, 2018
Zhou Yang, Long Nguyen, Fang Jin

Opioid addiction is a severe public health threat in the U.S, causing massive deaths and many social problems. Accurate relapse prediction is of practical importance for recovering patients since relapse prediction promotes timely relapse preventions that help patients stay clean. In this paper, we introduce a Generative Adversarial Networks (GAN) model to predict the addiction relapses based on sentiment images and social influences. Experimental results on real social media data from demonstrate that the GAN model delivers a better performance than comparable alternative techniques. The sentiment images generated by the model show that relapse is closely connected with two emotions `joy' and `negative'. This work is one of the first attempts to predict relapses using massive social media data and generative adversarial nets. The proposed method, combined with knowledge of social media mining, has the potential to revolutionize the practice of opioid addiction prevention and treatment.

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Beetle Swarm Optimization Algorithm:Theory and Application

Aug 01, 2018
Tiantian Wang, Long Yang, Qiang Liu

In this paper, a new meta-heuristic algorithm, called beetle swarm optimization algorithm, is proposed by enhancing the performance of swarm optimization through beetle foraging principles. The performance of 23 benchmark functions is tested and compared with widely used algorithms, including particle swarm optimization algorithm, genetic algorithm (GA) and grasshopper optimization algorithm . Numerical experiments show that the beetle swarm optimization algorithm outperforms its counterparts. Besides, to demonstrate the practical impact of the proposed algorithm, two classic engineering design problems, namely, pressure vessel design problem and himmelblaus optimization problem, are also considered and the proposed beetle swarm optimization algorithm is shown to be competitive in those applications.

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Transitive Hashing Network for Heterogeneous Multimedia Retrieval

Aug 15, 2016
Zhangjie Cao, Mingsheng Long, Qiang Yang

Hashing has been widely applied to large-scale multimedia retrieval due to the storage and retrieval efficiency. Cross-modal hashing enables efficient retrieval from database of one modality in response to a query of another modality. Existing work on cross-modal hashing assumes heterogeneous relationship across modalities for hash function learning. In this paper, we relax the strong assumption by only requiring such heterogeneous relationship in an auxiliary dataset different from the query/database domain. We craft a hybrid deep architecture to simultaneously learn the cross-modal correlation from the auxiliary dataset, and align the dataset distributions between the auxiliary dataset and the query/database domain, which generates transitive hash codes for heterogeneous multimedia retrieval. Extensive experiments exhibit that the proposed approach yields state of the art multimedia retrieval performance on public datasets, i.e. NUS-WIDE, ImageNet-YahooQA.

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3D Ken Burns Effect from a Single Image

Sep 12, 2019
Simon Niklaus, Long Mai, Jimei Yang, Feng Liu

The Ken Burns effect allows animating still images with a virtual camera scan and zoom. Adding parallax, which results in the 3D Ken Burns effect, enables significantly more compelling results. Creating such effects manually is time-consuming and demands sophisticated editing skills. Existing automatic methods, however, require multiple input images from varying viewpoints. In this paper, we introduce a framework that synthesizes the 3D Ken Burns effect from a single image, supporting both a fully automatic mode and an interactive mode with the user controlling the camera. Our framework first leverages a depth prediction pipeline, which estimates scene depth that is suitable for view synthesis tasks. To address the limitations of existing depth estimation methods such as geometric distortions, semantic distortions, and inaccurate depth boundaries, we develop a semantic-aware neural network for depth prediction, couple its estimate with a segmentation-based depth adjustment process, and employ a refinement neural network that facilitates accurate depth predictions at object boundaries. According to this depth estimate, our framework then maps the input image to a point cloud and synthesizes the resulting video frames by rendering the point cloud from the corresponding camera positions. To address disocclusions while maintaining geometrically and temporally coherent synthesis results, we utilize context-aware color- and depth-inpainting to fill in the missing information in the extreme views of the camera path, thus extending the scene geometry of the point cloud. Experiments with a wide variety of image content show that our method enables realistic synthesis results. Our study demonstrates that our system allows users to achieve better results while requiring little effort compared to existing solutions for the 3D Ken Burns effect creation.

* TOG 2019, 

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Synchronous Bidirectional Inference for Neural Sequence Generation

Feb 24, 2019
Jiajun Zhang, Long Zhou, Yang Zhao, Chengqing Zong

In sequence to sequence generation tasks (e.g. machine translation and abstractive summarization), inference is generally performed in a left-to-right manner to produce the result token by token. The neural approaches, such as LSTM and self-attention networks, are now able to make full use of all the predicted history hypotheses from left side during inference, but cannot meanwhile access any future (right side) information and usually generate unbalanced outputs in which left parts are much more accurate than right ones. In this work, we propose a synchronous bidirectional inference model to generate outputs using both left-to-right and right-to-left decoding simultaneously and interactively. First, we introduce a novel beam search algorithm that facilitates synchronous bidirectional decoding. Then, we present the core approach which enables left-to-right and right-to-left decoding to interact with each other, so as to utilize both the history and future predictions simultaneously during inference. We apply the proposed model to both LSTM and self-attention networks. In addition, we propose two strategies for parameter optimization. The extensive experiments on machine translation and abstractive summarization demonstrate that our synchronous bidirectional inference model can achieve remarkable improvements over the strong baselines.

* 27 pages 

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Online Hashing

Apr 06, 2017
Long-Kai Huang, Qiang Yang, Wei-Shi Zheng

Although hash function learning algorithms have achieved great success in recent years, most existing hash models are off-line, which are not suitable for processing sequential or online data. To address this problem, this work proposes an online hash model to accommodate data coming in stream for online learning. Specifically, a new loss function is proposed to measure the similarity loss between a pair of data samples in hamming space. Then, a structured hash model is derived and optimized in a passive-aggressive way. Theoretical analysis on the upper bound of the cumulative loss for the proposed online hash model is provided. Furthermore, we extend our online hashing from a single-model to a multi-model online hashing that trains multiple models so as to retain diverse online hashing models in order to avoid biased update. The competitive efficiency and effectiveness of the proposed online hash models are verified through extensive experiments on several large-scale datasets as compared to related hashing methods.

* To appear in IEEE Transactions on Neural Networks and Learning Systems (DOI: 10.1109/TNNLS.2017.2689242) 

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Low-rank SIFT: An Affine Invariant Feature for Place Recognition

Aug 07, 2014
Chao Yang, Shengnan Caih, Jingdong Wang, Long Quan

In this paper, we present a novel affine-invariant feature based on SIFT, leveraging the regular appearance of man-made objects. The feature achieves full affine invariance without needing to simulate over affine parameter space. Low-rank SIFT, as we name the feature, is based on our observation that local tilt, which are caused by changes of camera axis orientation, could be normalized by converting local patches to standard low-rank forms. Rotation, translation and scaling invariance could be achieved in ways similar to SIFT. As an extension of SIFT, our method seeks to add prior to solve the ill-posed affine parameter estimation problem and normalizes them directly, and is applicable to objects with regular structures. Furthermore, owing to recent breakthrough in convex optimization, such parameter could be computed efficiently. We will demonstrate its effectiveness in place recognition as our major application. As extra contributions, we also describe our pipeline of constructing geotagged building database from the ground up, as well as an efficient scheme for automatic feature selection.

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Learning to Transfer Examples for Partial Domain Adaptation

Apr 07, 2019
Zhangjie Cao, Kaichao You, Mingsheng Long, Jianmin Wang, Qiang Yang

Domain adaptation is critical for learning in new and unseen environments. With domain adversarial training, deep networks can learn disentangled and transferable features that effectively diminish the dataset shift between the source and target domains for knowledge transfer. In the era of Big Data, the ready availability of large-scale labeled datasets has stimulated wide interest in partial domain adaptation (PDA), which transfers a recognizer from a labeled large domain to an unlabeled small domain. It extends standard domain adaptation to the scenario where target labels are only a subset of source labels. Under the condition that target labels are unknown, the key challenge of PDA is how to transfer relevant examples in the shared classes to promote positive transfer, and ignore irrelevant ones in the specific classes to mitigate negative transfer. In this work, we propose a unified approach to PDA, Example Transfer Network (ETN), which jointly learns domain-invariant representations across the source and target domains, and a progressive weighting scheme that quantifies the transferability of source examples while controlling their importance to the learning task in the target domain. A thorough evaluation on several benchmark datasets shows that our approach achieves state-of-the-art results for partial domain adaptation tasks.

* CVPR 2019 accepted 

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Asymmetric Deep Semantic Quantization for Image Retrieval

Mar 29, 2019
Zhan Yang, Osolo Ian Raymond, WuQing Sun, Jun Long

Due to its fast retrieval and storage efficiency capabilities, hashing has been widely used in nearest neighbor retrieval tasks. By using deep learning based techniques, hashing can outperform non-learning based hashing in many applications. However, there are some limitations to previous learning based hashing methods (e.g., the learned hash codes are not discriminative due to the hashing methods being unable to discover rich semantic information and the training strategy having difficulty optimizing the discrete binary codes). In this paper, we propose a novel learning based hashing method, named \textbf{\underline{A}}symmetric \textbf{\underline{D}}eep \textbf{\underline{S}}emantic \textbf{\underline{Q}}uantization (\textbf{ADSQ}). \textbf{ADSQ} is implemented using three stream frameworks, which consists of one \emph{LabelNet} and two \emph{ImgNets}. The \emph{LabelNet} leverages three fully-connected layers, which is used to capture rich semantic information between image pairs. For the two \emph{ImgNets}, they each adopt the same convolutional neural network structure, but with different weights (i.e., asymmetric convolutional neural networks). The two \emph{ImgNets} are used to generate discriminative compact hash codes. Specifically, the function of the \emph{LabelNet} is to capture rich semantic information that is used to guide the two \emph{ImgNets} in minimizing the gap between the real-continuous features and discrete binary codes. By doing this, \textbf{ADSQ} can make full use of the most critical semantic information to guide the feature learning process and consider the consistency of the common semantic space and Hamming space. Results from our experiments demonstrate that \textbf{ADSQ} can generate high discriminative compact hash codes and it outperforms current state-of-the-art methods on three benchmark datasets, CIFAR-10, NUS-WIDE, and ImageNet.

* Submitted to IEEE ACCESS. arXiv admin note: text overlap with arXiv:1812.01404 

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Gradient Q$(σ, λ)$: A Unified Algorithm with Function Approximation for Reinforcement Learning

Sep 06, 2019
Long Yang, Yu Zhang, Qian Zheng, Pengfei Li, Gang Pan

Full-sampling (e.g., Q-learning) and pure-expectation (e.g., Expected Sarsa) algorithms are efficient and frequently used techniques in reinforcement learning. Q$(\sigma,\lambda)$ is the first approach unifies them with eligibility trace through the sampling degree $\sigma$. However, it is limited to the tabular case, for large-scale learning, the Q$(\sigma,\lambda)$ is too expensive to require a huge volume of tables to accurately storage value functions. To address above problem, we propose a GQ$(\sigma,\lambda)$ that extends tabular Q$(\sigma,\lambda)$ with linear function approximation. We prove the convergence of GQ$(\sigma,\lambda)$. Empirical results on some standard domains show that GQ$(\sigma,\lambda)$ with a combination of full-sampling with pure-expectation reach a better performance than full-sampling and pure-expectation methods.

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Scalable Differentially Private Generative Student Model via PATE

Jun 21, 2019
Yunhui Long, Suxin Lin, Zhuolin Yang, Carl A. Gunter, Bo Li

Recent rapid development of machine learning is largely due to algorithmic breakthroughs, computation resource development, and especially the access to a large amount of training data. However, though data sharing has the great potential of improving machine learning models and enabling new applications, there have been increasing concerns about the privacy implications of data collection. In this work, we present a novel approach for training differentially private data generator G-PATE. The generator can be used to produce synthetic datasets with strong privacy guarantee while preserving high data utility. Our approach leverages generative adversarial nets (GAN) to generate data and protect data privacy based on the Private Aggregation of Teacher Ensembles (PATE) framework. Our approach improves the use of privacy budget by only ensuring differential privacy for the generator, which is the part of the model that actually needs to be published for private data generation. To achieve this, we connect a student generator with an ensemble of teacher discriminators. We also propose a private gradient aggregation mechanism to ensure differential privacy on all the information that flows from the teacher discriminators to the student generator. We empirically show that the G-PATE significantly outperforms prior work on both image and non-image datasets.

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TBQ($σ$): Improving Efficiency of Trace Utilization for Off-Policy Reinforcement Learning

May 17, 2019
Longxiang Shi, Shijian Li, Longbing Cao, Long Yang, Gang Pan

Off-policy reinforcement learning with eligibility traces is challenging because of the discrepancy between target policy and behavior policy. One common approach is to measure the difference between two policies in a probabilistic way, such as importance sampling and tree-backup. However, existing off-policy learning methods based on probabilistic policy measurement are inefficient when utilizing traces under a greedy target policy, which is ineffective for control problems. The traces are cut immediately when a non-greedy action is taken, which may lose the advantage of eligibility traces and slow down the learning process. Alternatively, some non-probabilistic measurement methods such as General Q($\lambda$) and Naive Q($\lambda$) never cut traces, but face convergence problems in practice. To address the above issues, this paper introduces a new method named TBQ($\sigma$), which effectively unifies the tree-backup algorithm and Naive Q($\lambda$). By introducing a new parameter $\sigma$ to illustrate the \emph{degree} of utilizing traces, TBQ($\sigma$) creates an effective integration of TB($\lambda$) and Naive Q($\lambda$) and continuous role shift between them. The contraction property of TB($\sigma$) is theoretically analyzed for both policy evaluation and control settings. We also derive the online version of TBQ($\sigma$) and give the convergence proof. We empirically show that, for $\epsilon\in(0,1]$ in $\epsilon$-greedy policies, there exists some degree of utilizing traces for $\lambda\in[0,1]$, which can improve the efficiency in trace utilization for off-policy reinforcement learning, to both accelerate the learning process and improve the performance.

* 8 pages 

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Coordinating Disaster Emergency Response with Heuristic Reinforcement Learning

Nov 12, 2018
Long Nguyen, Zhou Yang, Jiazhen Zhu, Jia Li, Fang Jin

A crucial and time-sensitive task when any disaster occurs is to rescue victims and distribute resources to the right groups and locations. This task is challenging in populated urban areas, due to the huge burst of help requests generated in a very short period. To improve the efficiency of the emergency response in the immediate aftermath of a disaster, we propose a heuristic multi-agent reinforcement learning scheduling algorithm, named as ResQ, which can effectively schedule the rapid deployment of volunteers to rescue victims in dynamic settings. The core concept is to quickly identify victims and volunteers from social network data and then schedule rescue parties with an adaptive learning algorithm. This framework performs two key functions: 1) identify trapped victims and rescue volunteers, and 2) optimize the volunteers' rescue strategy in a complex time-sensitive environment. The proposed ResQ algorithm can speed up the training processes through a heuristic function which reduces the state-action space by identifying the set of particular actions over others. Experimental results showed that the proposed heuristic multi-agent reinforcement learning based scheduling outperforms several state-of-art methods, in terms of both reward rate and response times.

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