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Model-Based Stochastic Search for Large Scale Optimization of Multi-Agent UAV Swarms [article]

David D. Fan, Evangelos Theodorou, John Reeder
2018 arXiv   pre-print
We demonstrate the effectiveness of this approach on two complex multi-agent UAV swarm combat scenarios: where a team of fixed wing aircraft must attack a well-defended base, and where two teams of agents  ...  In this paper we show that Evolution Strategies are a special case of model-based stochastic search methods.  ...  Recently, a hybrid approach combining MPC and the use of genetic algorithms to evolve the cost function for a hand-tuned MPC controller has been demonstrated for a UAV swarm combat scenario [14] .  ... 
arXiv:1803.01106v2 fatcat:lx3qy5zahrbjnnyaynz64ffw5m

Table of Contents

2021 IEEE Transactions on Vehicular Technology  
Zhang 8108 Distributed Multi-Agent Target Tracking: A Nash-Combined Adaptive Differential Evolution Method for UAV Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ...  Hanzo 7812 Multi-Maneuvering Sources DOA Tracking With Improved Interactive Multi-Model Multi-Bernoulli Filter for Acoustic Vector Sensor (AVS) Array . . . . . . . . . . . . . . . . . . . . . . . . . .  ... 
doi:10.1109/tvt.2021.3100836 fatcat:hpsmhmav2zdh5ggtbe5r4xjoc4

Game Theory and Machine Learning in UAVs-Assisted Wireless Communication Networks: A Survey [article]

M. Zhou, Y. Guan, M. Hayajneh, K. Niu, C. Abdallah
2021 arXiv   pre-print
We also discuss how to combine game theory and machine learning for solving problems in U-WCNs and identify several future research directions.  ...  Existing surveys however focus either on game theory or machine learning and due to this fact, the current article surveys both game-theoretic and machine learning algorithms for use by UAVs in Wireless  ...  [110] proposed a multi-agent deep Q-learning method for multi-UAV trajectory design in a cellular Internet of UAVs.  ... 
arXiv:2108.03495v1 fatcat:g2gd64ugobalxmw5j74v7auaby

An Improved Approach towards Multi-Agent Pursuit–Evasion Game Decision-Making Using Deep Reinforcement Learning

Kaifang Wan, Dingwei Wu, Yiwei Zhai, Bo Li, Xiaoguang Gao, Zijian Hu
2021 Entropy  
A pursuit–evasion game is a classical maneuver confrontation problem in the multi-agent systems (MASs) domain.  ...  A control-oriented framework developed from the DRL-based multi-agent deep deterministic policy gradient (MADDPG) algorithm was built to implement multi-agent cooperative decision-making to overcome the  ...  JS20201100339) for providing the funding to conduct these experiments.  ... 
doi:10.3390/e23111433 pmid:34828131 pmcid:PMC8625563 fatcat:pht5mng7x5fz3ijfg46qhppxsy

Synchronous Reinforcement Learning-Based Control for Cognitive Autonomy

Kyriakos G. Vamvoudakis, Nick-Marios T. Kokolakis
2020 Foundations and Trends® in Systems and Control  
Introduction In this monograph we present a family of model-free, and modelbased online adaptive learning algorithms for single and multi-agent systems using measurements along the system trajectories  ...  Section 7 applies synchronous RL-based decision-making algorithms to motion planning in robotics as well as to coordinated target tracking using a team of bounded rational UAVs.  ... 
doi:10.1561/2600000022 fatcat:opy7x3dktfcd7i6d6uo3kuowwe

2019 Index IEEE Transactions on Systems, Man, and Cybernetics: Systems Vol. 49

2019 IEEE Transactions on Systems, Man & Cybernetics. Systems  
Adaptive Differential Evolution.  ...  ., +, TSMC April 2019 665-676 A Novel Real-Time Moving Target Tracking and Path Planning System for a Quadrotor UAV in Unknown Unstructured Outdoor Scenes.  ...  Open loop systems  ... 
doi:10.1109/tsmc.2019.2956665 fatcat:xhplbanlyne7nl7gp2pbrd62oi

2021 Index IEEE Transactions on Vehicular Technology Vol. 70

2021 IEEE Transactions on Vehicular Technology  
The Author Index contains the primary entry for each item, listed under the first author's name.  ...  ., +, TVT Feb. 2021 1146-1156 Distributed Multi-Agent Target Tracking: A Nash-Combined Adaptive Differential Evolution Method for UAV Systems.  ...  Perreault, M., +, TVT Feb. 2021 1146-1156 Distributed Multi-Agent Target Tracking: A Nash-Combined Adaptive Differential Evolution Method for UAV Systems.  ... 
doi:10.1109/tvt.2022.3151213 fatcat:vzuzqu54irebpibzp3ykgy5nca

Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms [article]

Kaiqing Zhang, Zhuoran Yang, Tamer Başar
2021 arXiv   pre-print
., the games of Go and Poker, robotics, and autonomous driving, involve the participation of more than one single agent, which naturally fall into the realm of multi-agent RL (MARL), a domain with a relatively  ...  the mean-field regime, (non-)convergence of policy-based methods for learning in games, etc.  ...  In particular, both the evolution of the system (a) Markov decision process (b) Markov game (c) Extensive-form game Figure 1 : Schematic diagrams for the system evolution of a Markov decision process,  ... 
arXiv:1911.10635v2 fatcat:ihlhtjlhnrdizbkcfzsnz5urfq

AI-Inspired Non-Terrestrial Networks for IIoT: Review on Enabling Technologies and Applications

Emmanouel T. Michailidis, Stelios M. Potirakis, Athanasios G. Kanatas
2020 IoT  
with significantly lower complexity compared to typical optimization methods.  ...  By adding intelligence and facilitating the decision-making and prediction procedures, the NTNs can effectively adapt to their surrounding environment, thus enhancing the performance of various metrics  ...  In this respect, a multi-agent deep Q-learning (DQL) approach was presented.  ... 
doi:10.3390/iot1010003 fatcat:xkjxfh6r2fd27jyuxazfc6lbqu

A Survey on Machine-Learning Techniques for UAV-Based Communications

Petros S Bithas, Emmanouel T Michailidis, Nikolaos Nomikos, Demosthenes Vouyioukas, Athanasios G Kanatas
2019 Sensors  
In this article, we provide a detailed survey of all relevant research works, in which ML techniques have been used on UAV-based communications for improving various design and functional aspects such  ...  In this context, the machine-learning (ML) framework is expected to provide solutions for the various problems that have already been identified when UAVs are used for communication purposes.  ...  Novel training methods are developed based on the combination of self-adaptive differential evolution (DE) algorithms with the Levenberg-Marquardt (LM) method.  ... 
doi:10.3390/s19235170 pmid:31779133 pmcid:PMC6929112 fatcat:pnur7lmpj5bj7poebmdfpd6bhi

Conference Guide [Front matter]

2020 2020 16th International Conference on Control, Automation, Robotics and Vision (ICARCV)  
paper studies the design of adaptive event-triggered protocols for consensus of linear multi-agent systems (MASs) with external disturbances.  ...  For conventional vehicle driving scenarios, we present a lightweight and targeted multi-sensor fusion method based on graph optimization, and implement the entire system on a smartphone.  ...  In this paper we investigate the resilient consensus problem for multi-agent systems under the specific attack scenarios where the attacker can eavesdrop on initial information of agents among the system  ... 
doi:10.1109/icarcv50220.2020.9305477 fatcat:4h7gpoj7ljgsrlkjoyw3qcfzxi

Drone Deep Reinforcement Learning: A Review

Ahmad Taher Azar, Anis Koubaa, Nada Ali Mohamed, Habiba A. Ibrahim, Zahra Fathy Ibrahim, Muhammad Kazim, Adel Ammar, Bilel Benjdira, Alaa M. Khamis, Ibrahim A. Hameed, Gabriella Casalino
2021 Electronics  
These algorithms targeted the guidance, navigation, and control (GNC) of UAVs.  ...  However, the use of UAVs in these applications needs a substantial level of autonomy.  ...  The control architecture can be described as a multi-agent one with decentralized control, while no models for the system are needed, and the behavior of the other agents is viewed from the perspective  ... 
doi:10.3390/electronics10090999 doaj:57ededb7d1a0445eaf34975cb6625c1f fatcat:kya3fbblszd27i4exlybnji4ni

When Autonomous Systems Meet Accuracy and Transferability through AI: A Survey

Chongzhen Zhang, Jianrui Wang, Gary G. Yen, Chaoqiang Zhao, Qiyu Sun, Yang Tang, Feng Qian, Jürgen Kurths
2020 Patterns  
Accuracy means that a well-trained model shows good results during the testing phase, in which the testing set shares a same task or a data distribution with the training set.  ...  Finally, we discuss several challenges and future topics for the use of adversarial learning, RL, and meta-learning in autonomous systems.  ...  ACKNOWLEDGMENTS The authors would like to thank the Editor-in-Chief, Scientific Editor, and anonymous referees for their helpful comments and suggestions, which have greatly improved this paper.  ... 
doi:10.1016/j.patter.2020.100050 pmid:33205114 pmcid:PMC7660378 fatcat:vs7wm2yrwjamjbaml36663wvze

UAV flight coordination for communication networks: genetic algorithms versus game theory

Alexandros Giagkos, Elio Tuci, Myra S. Wilson, Philip B. Charlesworth
2021 Soft Computing - A Fusion of Foundations, Methodologies and Applications  
The two methods consider realistic kinematics for hydrocarbon-powered medium-altitude, long-endurance aircrafts.  ...  In this paper, two systems are presented and compared based on their ability to reposition fixed-wing unmanned aerial vehicles to maintain a useful airborne wireless network topology.  ...  Çakıcı et al. (2016) present coordinated guidance for multi-UAV systems using GAs.  ... 
doi:10.1007/s00500-021-05863-6 fatcat:knutwt3nfjg3rhqdfq3znx3ay4

Models and Framework for Adversarial Attacks on Complex Adaptive Systems [article]

Vahid Behzadan, Arslan Munir
2017 arXiv   pre-print
We introduce the paradigm of adversarial attacks that target the dynamics of Complex Adaptive Systems (CAS).  ...  of targeting power grids, destabilization of terrorist organizations, and manipulation of machine learning agents.  ...  In particular, the Self-organization aspect of CAS enables the emergence of order and pattern from uncoordinated actions of autonomous agents in multi-agent distributed settings [9] .  ... 
arXiv:1709.04137v1 fatcat:risynvwcrffbddmogtwg5cmcli
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