Deep Q-Learning to Preserve Connectivity in Multi-robot Systems

Huang, Wanrong; Wang, Yanzhen; Yi, Xiaodong

doi:10.1145/3163080.3163113

Cited by 10 publications

(7 citation statements)

References 14 publications

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“…The reward is +1 if the algebraic connectivity of the system increases or holds, and becomes a penalty of -1 if the algebraic connectivity decreases. Similar to [127], a DQN is adopted. Due to the large action space of the followers, the actor-critic neural network [26] is used.…”

Section: B Connectivity Preservationmentioning

confidence: 99%

“…The simulation results show that the followers always follow the motion of the leaders even if the leaders' trajectory dynamically changes. However, the proposed DQN requires more time to converge than that in [127] because of the presence of more followers.…”

Section: B Connectivity Preservationmentioning

confidence: 99%

“…The proposed schemes in [127] and [128] do not consider a minimum distance between the leaders and followers. The leaders and followers can collide with each other if the distance between them is too short.…”

Section: B Connectivity Preservationmentioning

confidence: 99%

“…The DQL which allows each robot to make dynamic decisions based on its own state can be effectively applied to preserve the connectivity in the multirobot system. Such an approach is proposed in [127].…”

Section: B Connectivity Preservationmentioning

confidence: 99%

See 3 more Smart Citations

Applications of Deep Reinforcement Learning in Communications and Networking: A Survey

Luong

Hoang

Gong

et al. 2019

IEEE Commun. Surv. Tutorials

1,487

481

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This paper presents a comprehensive literature review on applications of deep reinforcement learning in communications and networking. Modern networks, e.g., Internet of Things (IoT) and Unmanned Aerial Vehicle (UAV) networks, become more decentralized and autonomous. In such networks, network entities need to make decisions locally to maximize the network performance under uncertainty of network environment. Reinforcement learning has been efficiently used to enable the network entities to obtain the optimal policy including, e.g., decisions or actions, given their states when the state and action spaces are small. However, in complex and large-scale networks, the state and action spaces are usually large, and the reinforcement learning may not be able to find the optimal policy in reasonable time. Therefore, deep reinforcement learning, a combination of reinforcement learning with deep learning, has been developed to overcome the shortcomings. In this survey, we first give a tutorial of deep reinforcement learning from fundamental concepts to advanced models. Then, we review deep reinforcement learning approaches proposed to address emerging issues in communications and networking. The issues include dynamic network access, data rate control, wireless caching, data offloading, network security, and connectivity preservation which are all important to next generation networks such as 5G and beyond. Furthermore, we present applications of deep reinforcement learning for traffic routing, resource sharing, and data collection. Finally, we highlight important challenges, open issues, and future research directions of applying deep reinforcement learning.

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Section: B Connectivity Preservationmentioning

confidence: 99%

Section: B Connectivity Preservationmentioning

confidence: 99%

Section: B Connectivity Preservationmentioning

confidence: 99%

Section: B Connectivity Preservationmentioning

confidence: 99%

See 2 more Smart Citations

Applications of Deep Reinforcement Learning in Communications and Networking: A Survey

Luong

Hoang

Gong

et al. 2019

IEEE Commun. Surv. Tutorials

1,487

481

View full text Add to dashboard Cite

show abstract

“…Reinforcement learning is widely used in UAV communication and networking, which helps UAVs to make intelligent local and autonomous decisions [ 11 , 12 , 13 ]. Various problems such as connectivity maintenance [ 14 ], traffic routing [ 15 ], data collection [ 16 ] and caching and offloading [ 17 ] employed reinforcement learning to improve the efficiency and the autonomy of the networks. In this section, we review existing RL and Deep-RL related works on drones in more detail.…”

Section: Related Workmentioning

confidence: 99%

Deep Q-Learning for Two-Hop Communications of Drone Base Stations

Fotouhi

Ding

Hassan

2021

Sensors

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In this paper, we address the application of the flying Drone Base Stations (DBS) in order to improve the network performance. Given the high degrees of freedom of a DBS, it can change its position and adapt its trajectory according to the users movements and the target environment. A two-hop communication model, between an end-user and a macrocell through a DBS, is studied in this work. We propose Q-learning and Deep Q-learning based solutions to optimize the drone’s trajectory. Simulation results show that, by employing our proposed models, the drone can autonomously fly and adapts its mobility according to the users’ movements. Additionally, the Deep Q-learning model outperforms the Q-learning model and can be applied in more complex environments.

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Spectrum-Agile Cognitive Interference Avoidance Through Deep Reinforcement Learning

Aref

Jayaweera

2019

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Deep Q-Learning to Preserve Connectivity in Multi-robot Systems

Cited by 10 publications

References 14 publications

Applications of Deep Reinforcement Learning in Communications and Networking: A Survey

Applications of Deep Reinforcement Learning in Communications and Networking: A Survey

Deep Q-Learning for Two-Hop Communications of Drone Base Stations

Spectrum-Agile Cognitive Interference Avoidance Through Deep Reinforcement Learning

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