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Found 4 papers, 1 papers with code
Evolving Pareto-Optimal Actor-Critic Algorithms for Generalizability and Stability
Generalizability and stability are two key objectives for operating reinforcement learning (RL) agents in the real world.
Towards Autonomous Satellite Communications: An AI-based Framework to Address System-level Challenges
The next generation of satellite constellations is designed to better address the future needs of our connected society: highly-variable data demand, mobile connectivity, and reaching more under-served regions.
Integrating LSTMs and GNNs for COVID-19 Forecasting
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
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.
