Reinforcement Learning Based Virtual Backbone Construction in Manet Using Connected Dominating Sets

Prasanna, D.S. John Deva; Aravindhar, D. John; Sivasankar, P.; Perumal, Karthickeyan

doi:10.31838/jcr.07.09.28

jcr

2020

DOI: 10.31838/jcr.07.09.28

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Reinforcement Learning Based Virtual Backbone Construction in Manet Using Connected Dominating Sets

D.S. John Deva Prasanna¹,

D. John Aravindhar²,

P. Sivasankar³

et al.

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2023

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Review of Metrics to Measure the Stability, Robustness and Resilience of Reinforcement Learning

Pullum¹

2023

Machine Learning and Soft Computing

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Reinforcement learning (RL) has received significant interest in recent years, primarily because of the success of deep RL in solving many challenging tasks, such as playing chess, Go, and online computer games. However, with the increasing focus on RL, applications outside gaming and simulated environments require an understanding of the robustness, stability, and resilience of RL methods. To this end, we conducted a comprehensive literature review to characterize the available literature on these three behaviors as they pertain to RL. We classified the quantitative and theoretical approaches used to indicate or measure robustness, stability, and resilience behaviors. In addition, we identified the actions or events to which the quantitative approaches attempted to be stable, robust, or resilient. Finally, we provide a decision tree that is useful for selecting metrics to quantify behavior. We believe that this is the first comprehensive review of stability, robustness, and resilience, specifically geared toward RL.

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Review of Metrics to Measure the Stability, Robustness and Resilience of Reinforcement Learning

Pullum¹

2023

Machine Learning and Soft Computing

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Reinforcement Learning Based Virtual Backbone Construction in Manet Using Connected Dominating Sets

Cited by 1 publication

References 0 publications

Review of Metrics to Measure the Stability, Robustness and Resilience of Reinforcement Learning

Review of Metrics to Measure the Stability, Robustness and Resilience of Reinforcement Learning

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