A deep reinforcement learning approach to preserve connectivity for multi-robot systems

Huang, Wanrong; Wang, Yanzhen; Yi, Xiaodong

doi:10.1109/cisp-bmei.2017.8302157

Cited by 6 publications

(6 citation statements)

References 11 publications

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“…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%

“…Considering the general scenario, the authors in [128] address the connectivity preservation between multiple leaders and multiple followers. The robot system is definitely connected if any two robots are connected via a direct link or multi-hop link.…”

Section: B Connectivity Preservationmentioning

confidence: 99%

“…In particular, if the leader is too close to its follower, the reward of the system is penalized regarding the minimum distance. The DQL algorithm proposed in [128] is then used such that the BS learns proper actions, e.g., turning left and right, to maximize the cumulative reward.…”

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

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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%

See 1 more Smart Citation

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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show abstract

“…Actor-critic method and DQN have been applied in communication networks to maintain connectivity. Few examples on application of RL related to preserving connectivity are: [57], [206] [207], [208].…”

Section: Communication and Networkingmentioning

confidence: 99%

A Gentle Introduction to Reinforcement Learning and its Application in Different Fields

Naeem

Rizvi

Coronato³

2020

IEEE Access

146

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Due to the recent progress in Deep Neural Networks, Reinforcement Learning (RL) has become one of the most important and useful technology. It is a learning method where a software agent interacts with an unknown environment, selects actions, and progressively discovers the environment dynamics. RL has been effectively applied in many important areas of real life. This article intends to provide an in-depth introduction of the Markov Decision Process, RL and its algorithms. Moreover, we present a literature review of the application of RL to a variety of fields, including robotics and autonomous control, communication and networking, natural language processing, games and self-organized system, scheduling management and configuration of resources, and computer vision.

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“…Solutions might be found in A3C's action strategy. In stochastic differential equations (SDE) [27], the white noise N t is often seen as the "derivative" of the Brownian motion B t [28], so the total derivative of A3C algorithm can be written as: η t dt = µ θ (s t ) dt + σ θ (s t ) • dB t . This gives a starting point for our research.…”

Section: Introductionmentioning

confidence: 99%

Incremental Reinforcement Learning --- a New Continuous Reinforcement Learning Frame Based on Stochastic Differential Equation methods

Chen,

Cheng,

Liu

et al. 2019

Preprint

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A deep reinforcement learning approach to preserve connectivity for multi-robot systems

Cited by 6 publications

References 11 publications

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

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

A Gentle Introduction to Reinforcement Learning and its Application in Different Fields

Incremental Reinforcement Learning --- a New Continuous Reinforcement Learning Frame Based on Stochastic Differential Equation methods

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