Summary of Continual Deep Reinforcement Learning For Decentralized Satellite Routing, by Federico Lozano-cuadra et al.
Continual Deep Reinforcement Learning for Decentralized Satellite Routing
by Federico Lozano-Cuadra, Beatriz Soret, Israel Leyva-Mayorga, Petar Popovski
First submitted to arxiv on: 20 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Information Theory (cs.IT)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces a full solution for decentralized routing in Low Earth Orbit satellite constellations based on continual Deep Reinforcement Learning (DRL). The authors address multiple challenges, including partial knowledge at satellites and continuous movement, time-varying sources of uncertainty such as traffic, communication links or buffers. They propose a multi-agent approach where each satellite acts as an independent decision-making agent with limited environmental knowledge acquired from nearby agents. The solution consists of two phases: offline learning using decentralized decisions and a global DNN trained with global experiences; and online phase with local, on-board, and pre-trained DNNs that require continual learning to evolve with the environment. The results show that the proposed Multi-Agent DRL framework achieves similar E2E performance as a shortest-path solution without high congestion, but adapts well to congestion conditions and exploits less loaded paths. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, scientists developed a new way for satellites in space to communicate effectively. They used a special type of learning called Deep Reinforcement Learning (DRL) that helps make decisions based on limited information. The system is designed to work with multiple moving satellites that have different amounts of knowledge about the environment. The team came up with two ways to make this happen: one way involves predicting what will happen next, and another way uses a technique called Federated Learning (FL) where all the satellite’s models are combined. They tested their system and found it works well in most situations. |
Keywords
» Artificial intelligence » Continual learning » Federated learning » Reinforcement learning
