This work provides the RL method with a negative reward as long as any SO has a total position
error above the uncertainty threshold. This penalizes policies that take longer to achieve …

… space assets and ensure sustainable space usage. In this work, deep reinforcement learning
… of dimensionality inherent in the space situational awareness sensor tasking problem. An in…

… reinforcement learning (DRL) methods are leveraged to overcome the curse of dimensionality
that is inherent in space situational awareness … and population-based training with an in-…

… To maintain a robust catalog of resident space objects (RSOs), space situational awareness
(SSA) mission operators depend on ground- and space-based sensors to repeatedly detect, …

… The Space Situational Awareness mission that had been carried out by USSPACECOM for
decades has relied on radars and optical sensors to track and catalog these resident space …

… reinforcement learning. DRL agents rely on a different learning paradigm compared to
traditional machine learning … For example, by considering the nuances of reinforcement learning, …

… of this research is to use deep reinforcement learning technique to create an autonomous
sensor tasking system capable of meeting the needs for Space Situational Awareness. …

… A foundational element of Space Situational Awareness (SSA) is the buildup and
maintenance of a catalog of the resident space objects (RSOs) orbiting the Earth. The object …

… of deep learning methods for space situational awareness (SSA) … Reinforcement Learning
based framework to be explored in future research in the field of space situational awareness…

… Recent studies have shown promising potential of Deep Reinforcement Learning (DRL) for
… and show that the state space can be reduced and the learning efficiency of DRL agent can …