Applicability and Challenges of Deep Reinforcement Learning for
Satellite Frequency Plan Design
- URL: http://arxiv.org/abs/2010.08015v2
- Date: Tue, 12 Jan 2021 16:40:00 GMT
- Title: Applicability and Challenges of Deep Reinforcement Learning for
Satellite Frequency Plan Design
- Authors: Juan Jose Garau Luis, Edward Crawley and Bruce Cameron
- Abstract summary: Deep Reinforcement Learning (DRL) models have become a trend in many industries, including aerospace engineering and communications.
This paper explores the tradeoffs of different elements of DRL models and how they might impact the final performance.
No single DRL model is able to outperform the rest in all scenarios, and the best approach for each the 6 core elements depends on the features of the operation environment.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: The study and benchmarking of Deep Reinforcement Learning (DRL) models has
become a trend in many industries, including aerospace engineering and
communications. Recent studies in these fields propose these kinds of models to
address certain complex real-time decision-making problems in which classic
approaches do not meet time requirements or fail to obtain optimal solutions.
While the good performance of DRL models has been proved for specific use cases
or scenarios, most studies do not discuss the compromises and generalizability
of such models during real operations. In this paper we explore the tradeoffs
of different elements of DRL models and how they might impact the final
performance. To that end, we choose the Frequency Plan Design (FPD) problem in
the context of multibeam satellite constellations as our use case and propose a
DRL model to address it. We identify 6 different core elements that have a
major effect in its performance: the policy, the policy optimizer, the state,
action, and reward representations, and the training environment. We analyze
different alternatives for each of these elements and characterize their
effect. We also use multiple environments to account for different scenarios in
which we vary the dimensionality or make the environment nonstationary. Our
findings show that DRL is a potential method to address the FPD problem in real
operations, especially because of its speed in decision-making. However, no
single DRL model is able to outperform the rest in all scenarios, and the best
approach for each of the 6 core elements depends on the features of the
operation environment. While we agree on the potential of DRL to solve future
complex problems in the aerospace industry, we also reflect on the importance
of designing appropriate models and training procedures, understanding the
applicability of such models, and reporting the main performance tradeoffs.
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