13 citations · 26 across the 5 of their papers we have counts for
5 papers
Integrating LSTMs and GNNs for COVID-19 Forecasting
Nathan Sesti, Juan Jose Garau-Luis, Edward Crawley +1
The spread of COVID-19 has coincided with the rise of Graph Neural Networks (GNNs), leading to several studies proposing their use to better forecast the evolution of the pandemic.…
Evaluating the progress of Deep Reinforcement Learning in the real world: aligning domain-agnostic and domain-specific research
Juan Jose Garau-Luis, Edward Crawley, Bruce Cameron
Deep Reinforcement Learning (DRL) is considered a potential framework to improve many real-world autonomous systems; it has attracted the attention of multiple and diverse fields.…
Policy Gradient RL Algorithms as Directed Acyclic Graphs
Juan Jose Garau Luis
Meta Reinforcement Learning (RL) methods focus on automating the design of RL algorithms that generalize to a wide range of environments. The framework introduced in (Anonymous, 20…
Applicability and Challenges of Deep Reinforcement Learning for Satellite Frequency Plan Design
Juan Jose Garau Luis, Edward Crawley, Bruce Cameron
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…
Deep Reinforcement Learning Architecture for Continuous Power Allocation in High Throughput Satellites
Juan Jose Garau Luis, Markus Guerster, Inigo del Portillo +2
In the coming years, the satellite broadband market will experience significant increases in the service demand, especially for the mobility sector, where demand is burstier. Many…