This paper introduces a model-based approach for training feedback controllers for an
autonomous agent operating in a highly nonlinear (albeit deterministic) environment. We …

Reinforcement learning (RL) has revolutionized decision-making across a wide range of
domains over the past few decades. Yet, deploying RL policies in real-world scenarios …

We study reinforcement learning (RL) for decision processes with non-Markovian reward, in
which high-level knowledge in the form of reward machines is available to the learner …

This article introduces a model-based approach for training feedback controllers for an
autonomous agent operating in a highly non-linear (albeit deterministic) environment. We …

Learning-enabled controllers have been adopted in various cyber-physical systems (CPS).
When a learning-enabled controller fails to accomplish its task from a set of initial states …

Reinforcement learning (RL) has revolutionized decision-making across a wide range of
domains over the past few decades. Yet, deploying RL policies in real-world scenarios …

As the number of spacecraft on orbit continues to grow, it is challenging for human operators
to constantly monitor and plan for all missions. Autonomous control methods such as …

Reinforcement learning (RL) is a powerful technique for learning optimal policies from trial
and error. However, designing a reward function that captures the desired behavior of an …

Signal temporal logic (STL) formulas have been widely used as a formal language to
express complex robotic specifications, thanks to their rich expressiveness and explicit time …

As the number of spacecraft on orbit continues to grow, it is challenging for human operators
to constantly monitor and plan for all missions. Autonomous control methods such as …