Stochastic hybrid systems have received significant attentions as a relevant modeling
framework describing many systems, from engineering to the life sciences: they enable the …

Reinforcement Learning (RL) is a widely employed machine learning architecture that has
been applied to a variety of control problems. However, applications in safety-critical …

This paper proposes DeepSynth, a method for effective training of deep reinforcement
learning agents when the reward is sparse or non-Markovian, but at the same time progress …

From grading papers to summarizing medical documents, large language models (LLMs)
are evermore used for evaluation of text generated by humans and AI alike. However …

This paper addresses the problem of maintaining safety during training in Reinforcement
Learning (RL), such that the safety constraint violations are bounded at any point during …

Safe exploration aims at addressing the limitations of Reinforcement Learning (RL) in safety-
critical scenarios, where failures during trial-and-error learning may incur high costs. Several …

This paper addresses the problem of learning optimal control policies for systems with
uncertain dynamics and high-level control objectives specified as Linear Temporal Logic …

Reinforcement learning agents naturally learn from extensive exploration. Exploration is
costly and can be unsafe in $\textit {safety-critical} $ domains. This paper proposes a novel …

This paper addresses the problem of designing optimal control policies for mobile robots
with mission and safety requirements specified using Linear Temporal Logic (LTL). We …

Several methods have been proposed recently to learn neural network (NN) controllers for
autonomous agents, with unknown and stochastic dynamics, tasked with complex missions …