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14th IEEE International Conference on Advanced Infocomm Technology (ICAIT)

作者： Yang, Bei Xu, Yiqian She, Xiaoming Zhu, Jianchi Wei, Fengsheng Cheri, Peng Wang, Jianxiu China Telecom Res Inst Beijing 102209 Peoples R China Univ Elect Sci & Technol China Chengdu 611731 Peoples R China

ISBN: (数字)9781665471565

ISBN: (纸本)9781665471565

With the rapid development of the Internet of Things (IoT) systems, the low latency requirement of massive Machine Type Communication ( mMTC) in the IoT is an urgent problem to be solved for future mobile communication networks. In this paper, we use a reasonable resource allocation strategy and set priority parameters for each slice according to the average access delay of each slice. We propose a dynamic resource allocation strategy based on Markov Decision Process (MDP) modeling of mMTC random access process and using actor-critic (AC) algorithm in reinforcement learning. Simulations show that the proposed resource block resource allocation algorithm can reasonably allocate resources for each mMTC access slice to ensure the Quality-of-Service (QoS) requirements of mMTC applications.

关键词： random access network slicing mMTC actor-critic algorithm

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Fast and Accurate Trajectory Tracking for Unmanned Aerial Vehicles based on Deep Reinforcement Learning 25

Fast and Accurate Trajectory Tracking for Unmanned Aerial Ve...

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25th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA)

作者： Li, Yilan Li, Hongjia Li, Zhe Fang, Haowen Sanyal, Amit K. Wang, Yanzhi Qiu, Qinru Syracuse Univ Elect Engn & Comp Sci Syracuse NY 13244 USA Syracuse Univ Mech & Aerosp Engn Syracuse NY 13244 USA Northeastern Univ Elect & Comp Engn Boston MA 02115 USA

ISBN: (纸本)9781728131979

Continuous trajectory control of fixed-wing unmanned aerial vehicles (UAVs) is complicated when considering hidden dynamics. Due to UAV multi degrees of freedom, tracking methodologies based on conventional control theory, such as Proportional-Integral-Derivative (PID) has limitations in response time and adjustment robustness, while a model based approach that calculates the force and torques based on UAV's current status is complicated and rigid. We present an actor-critic reinforcement learning framework that controls UAV trajectory through a set of desired waypoints. A deep neural network is constructed to learn the optimal tracking policy and reinforcement learning is developed to optimize the resulting tracking scheme. The experimental results show that our proposed approach can achieve 58.14% less position error, 21.77% less system power consumption and 9.23% faster attainment than the baseline. The actor network consists of only linear operations, hence Field Programmable Gate Arrays (FPGA) based hardware acceleration can easily be designed for energy efficient real-time control.

关键词： deep reinforcement learning continuous trajectory tracking actor-critic algorithm unmanned aerial vehicles

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A Neuro-fuzzy Learning System for Adaptive Swarm Behaviors Dealing with Continuous State Space

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4th International Conference on Intelligent Computing

作者： Kuremoto, Takashi Obayashi, Masanao Kobayashi, Kunikazu Adachi, Hirotaka Yoneda, Kentaro Yamaguchi Univ Grad Sch Sci & Engn Tokiwadai 2-16-1 Yamaguchi 7558611 Japan Fac Sci & Engn Yamaguchi Japan

ISBN: (纸本)9783540859833

Swarm intelligence has brought a new paradise for function optimization, structural optimization, multi-agent systems and other study fields. In our previous work, we proposed a neuro-fuzzy system using reinforcement learning algorithm (actor-critic method with TD error learning algorithm) to acquire optimized swarm behaviors. This paper improves the conventional learning system, which only deals with discrete state space and action space, to solve how a swarm to learn and obtain its adaptive behaviors in the continuous state space. The improved system adopts a new policy function of action which is possible to yield continuous actions corresponding to continuous states. The effectiveness of proposed system is investigated by computer simulations with more kinds of environments for the goal-exploration problem.

关键词： neuro-fuzzy net swarm behavior reinforcement learning multi-agent system actor-critic algorithm goal-exploration problem

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Optimal Transportation Network Company Vehicle Dispatching via Deep Deterministic Policy Gradient 14th

Optimal Transportation Network Company Vehicle Dispatching v...

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14th International Conference on Wireless algorithms, Systems, and Applications (WASA)

作者： Shi, Dian Li, Xuanheng Li, Ming Wang, Jie Li, Pan Pan, Miao Univ Houston Houston TX 77204 USA Dalian Univ Technol Dalian Peoples R China Univ Texas Arlington Arlington TX 76019 USA Case Western Reserve Univ Cleveland OH 44106 USA

ISBN: (纸本)9783030235970;9783030235963

With the popularity of smart phones and the maturity of civilian global positioning system (GPS) technology, transportation network company (TNC) services have become a prominent commute mode in many major cities, which can effectively pair the passengers with the TNC vehicles/drivers through mobile applications. However, given the growing number of TNC vehicles, how to efficiently dispatch TNC vehicles poses crucial challenges. In this paper, we propose a novel method for TNC vehicle dispatching in different areas of the city based on deep reinforcement learning (DRL) method with joint consideration of the TNC company, individual TNC vehicle, and customer/passenger. The proposed model optimizes the distribution of vehicles geographically to meet the customers' demands, while improving the drivers' profit. In particular, we consider the high dimensional state and action space in the urban city traffic dynamic environment, and develop a deep deterministic policy gradient, an actor-critic based DRL algorithm for dispatching vacant TNC vehicles. We leverage Didi Chuxing's open data set to evaluate the performance of the proposed approach, and the simulation results show that the proposed approach improves the average income of the driver while satisfying the supply and demand relationship between TNC vehicles and customers/passengers.

关键词： TNC services TNC vehicle dispatching actor-critic algorithm Deterministic policy gradient

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Content-centric Caching Using Deep Reinforcement Learning in Mobile Computing

Content-centric Caching Using Deep Reinforcement Learning in...

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International Conference on High Performance Big Data and Intelligent Systems (HPBD&IS)

作者： Wang, Cairong Gai, Keke Guo, Jinnan Zhu, Liehuang Zhang, Zijian Beijing Inst Technol Sch Comp Sci & Technol Beijing Peoples R China Beijing Inst Technol AffBSch Informat & Elect Beijing Peoples R China Univ Auckland Dept Comp Sci Auckland New Zealand

ISBN: (纸本)9781728104669

In era of Internet, the amount of the connected devices has been remarkably increasing along with the increment of the network-based service. Both service quality and user's experience are facing great impact from latency issue while a large volume of concurrent user requests are made in the context of mobile computing. Deploying caching techniques at base stations or edge nodes is an alternative for dealing with the latency time issue. However, traditional caching techniques, e.g. Least Recently Used (LRU) or Least Frequently Used (LFU), cannot efficiently resolve latency caused by the complex content-oriented popularity distribution. In this paper, we propose a Deep Reinforcement Learning (DPL)-based approach to make the caching storage adaptable for dynamic and complicated mobile networking environment. The proposed mechanism does not need priori knowledge of the popularity distribution, so that it has a higher-level adoptability and flexibility in practice, compared with LRU and LFU. Our evaluation also compares the proposed approach with other deep learning methods and the results have suggested that our approach has a higher accuracy.

关键词： deep reinforcement learning actor-critic algorithm content caching mobile computing

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A Deep Reinforcement Learning Approach for Navigation and Control of Autonomous Underwater Vehicles in Complex Environments 18

A Deep Reinforcement Learning Approach for Navigation and Co...

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18th International Conference on Control Automation Robotics and Vision

作者： Stefanidou, Artemis Politi, Elena Chronis, Christos Dimitrakopoulos, George Varlamis, Iraklis Harokopio Univ Dept Informat & Telemat Athens Greece

ISBN: (纸本)9798331518509;9798331518493

The comprehension of the underwater environment is recently being accelerated by technological advances in sensors, robotics and Artificial Intelligence (AI). At the forefront of this evolution, lies the Autonomous Underwater Vehicle (AUV), a sophisticated ocean exploration tool that is capable of performing underwater mapping, leveraging data obtained by onboard sensors. AUVs can navigate autonomously in unknown environments without any human interaction, while their level of autonomy is tightly linked to their path planning strategy. In this study, we perform a comparative analysis of a Deep Reinforcement Learning (DRL) method utilising two neural network models, a Linear Model (LM) that consists only of linear layers, and a Convolutional Model (CM) that consists of convolution layers for feature extraction that are merged with linear layers. Our evaluation focuses on assessing the performance of the proposed models for generating optimal paths in 3D underwater environments based on path length and obstacle avoidance. Through comprehensive simulations, we showcase the efficiency of our solution and present a comprehensive framework tailored for solving path planning problems in 3D complex underwater settings.

关键词： AUV actor-critic algorithm AUV navigation dynamic path planning reinforcement learning

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Transfer Reinforcement Learning Framework for Energy Saving in Next Generation Wireless Networks

Transfer Reinforcement Learning Framework for Energy Saving ...

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作者： Shreyata Sharma Indraprastha Institute of Information Technology Delhi

学位级别：硕士

Recent upsurge in data intensive applications over wireless communication networks is stimu- lating rapid expansion of such networks and thus presenting new research challenges pertaining to their efficient deployment. In the present communication networks, the increased traffic load entails network operators to expand their networks by the deployment of a large number of base stations (BSs) and access points (APs). Studies have reported that a major portion of energy consumption occurs at the access network entities. This means that the massive data traffic is being served at the expense of increased carbon footprint and huge energy consumption. There- fore, energy saving has emerged as one of the major aspects in such data intensive and high traffic communication networks. Considering this, the energy efficient operation of BSs and APs has become a major research problem and it is well taken up in this thesis for the case of wireless networks. In this work, the research aim of energy saving has been considered for both, cellular BSs and Wi-Fi APs to cover the major part of the wireless communication networks. An actor-critic (AC) reinforcement learning (RL) framework is used to enable traffic based ON/OFF switching of BSs and APs. Furthermore, previously estimated traffic statistics is exploited through the process of transfer learning for further improvement in energy savings and speeding up the learning process. Herein, this novel approach is used for three cases: realization of a transfer learning framework for Wi-Fi networks, implementation of a three state RL based BS switching scheme for existing cellular networks and application of RL in heterogeneous networks (HetNets) consisting of macro and femto BSs. The use of practical scenario and real time data collected from institute's Wi-Fi network to validate the adopted scheme is an important feature of this study. The superiority of the proposed framework is depicted through simulations and relevant mathematic

关键词： Reinforcement learning transfer learning actor-critic algorithm energy efficient access networks moderate traffic heterogeneous networks

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Dispatch of autonomous vehicles for taxi services: A deep reinforcement learning approach

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TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2020年 115卷 102626-102626页

作者： Mao, Chao Liu, Yulin Shen, Zuo-Jun (max) Univ Calif Berkeley Dept Civil & Environm Engn Berkeley CA 94720 USA Univ Calif Berkeley Dept Ind Engn & Operat Res Berkeley CA 94720 USA Tsinghua Univ Tsinghua Berkeley Shenzhen Inst TBSI Shenzhen 518055 Peoples R China

In this paper, we define and investigate a novel model-free deep reinforcement learning framework to solve the taxi dispatch problem. The framework can be used to redistribute vehicles when the travel demand and taxi supply is either spatially or temporally imbalanced in a transportation network. While previous works mostly focus on using model-based methods, the goal of this paper is to explore the policy-based deep reinforcement learning algorithm as a model-free method to optimize the rebalancing strategy. In particular, we propose an actor-critic algorithm with feed-forward neural networks as approximations of both policy and value functions, where the policy function provides the optimal dispatch strategy and the value function estimates the expected costs at each time stamp. Our numerical studies show that the algorithm converges to the theoretical upper bound with less than 4% optimality gap, whether the system dynamics are deterministic or stochastic. We also investigate the scenario where we consider user priority and fairness, and the results indicate that our learned policy is capable of producing a superior strategy that balances equity, cancellation, and level of service when user priority is considered.

关键词： Taxi dispatching Demand rebalancing Deep reinforcement learning actor-critic algorithm Policy-gradient Autonomous vehicles Ride sharing

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TASAC: A twin-actor reinforcement learning framework with a stochastic with an to batch control

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CONTROL ENGINEERING PRACTICE 2023年第1期134卷

作者： Joshi, Tanuja Kodamana, Hariprasad Kandath, Harikumar Kaisare, Niket Indian Inst Technol Delhi Dept Chem Engn New Delhi 110016 India Indian Inst Technol Delhi Yardi Sch Artificial Intelligence New Delhi 110016 India Int Inst Informat Technol Hyderabad Hyderabad 500032 India Indian Inst Technol Madras Dept Chem Engn Chennai 600036 India

Due to their complex nonlinear dynamics and batch-to-batch variability, batch processes pose a challenge for process control. Due to the absence of accurate models and resulting plant-model mismatch, these problems become harder to address for advanced model-based control strategies. Reinforcement Learning (RL), wherein an agent learns the policy by directly interacting with the environment, offers a potential alternative in this context. RL frameworks with actor-critic architecture have recently become popular for controlling systems where state and action spaces are continuous. The current study proposes a stochastic actor-critic RL algorithm, termed Twin actor Soft actor-critic (TASAC), by incorporating an ensemble of actors in a maximum entropy framework to improve learning due to enhanced exploration. The efficacy of the proposed approach is showcased by applying the same for the control of batch transesterification.

关键词： Reinforcement learning actor-critic algorithm Deep learning Batch process

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Multi-agent reinforcement learning for multi-area power exchange

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ELECTRIC POWER SYSTEMS RESEARCH 2024年 235卷

作者： Xi, Jiachen Garcia, Alfredo Chen, Yu Christine Khatami, Roohallah Texas A&M Univ Dept Ind & Syst Engn College Stn TX 77840 USA Univ British Columbia Dept Elect & Comp Engn Vancouver BC Canada Southern Illinois Univ Sch Elect Comp & Biomed Engn Carbondale IL USA

Increasing renewable integration leads to faster and more frequent fluctuations in the power system net-load (load minus non-dispatchable renewable generation) along with greater uncertainty in its forecast. These can exacerbate the computational burden of centralized power system optimization (or market clearing) that accounts for variability and uncertainty in net load. Another layer of complexity pertains to estimating accurate models of spatio-temporal net-load uncertainty. Taken together, decentralized approaches for learning to optimize (or to clear a market) using only local information are compelling to explore. This paper develops a decentralized multi-agent reinforcement learning (MARL) approach that seeks to learn optimal policies for operating interconnected power systems under uncertainty. The proposed method incurs less computational and communication burden compared to a centralized stochastic programming approach and offers improved privacy preservation. Numerical simulations involving a three-area test system yield desirable results, with the resulting average net operation costs being less than 5% away from those obtained in a benchmark centralized model predictive control solution.

关键词： Power system Reinforcement learning Uncertainty Decentralized algorithm actor-critic algorithm

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