Reinforcement Learning (RL) has achieved tremendous success in many complex decision-
making tasks. However, safety concerns are raised during deploying RL in real-world …

We survey recent work on neurosymbolic programming, an emerging area that bridges the
areas of deep learning and program synthesis. Like in classic machine learning, the goal …

This paper presents the Neural Network Verification (NNV) software tool, a set-based
verification framework for deep neural networks (DNNs) and learning-enabled cyber …

Although deep reinforcement learning has become a promising machine learning approach
for sequential decision-making problems, it is still not mature enough for high-stake domains …

Artificial Intelligence (AI) algorithms are increasingly providing decision making and
operational support across multiple domains. AI includes a wide (and growing) library of …

This paper concerns the efficient construction of a safety shield for reinforcement learning.
We specifically target scenarios that incorporate uncertainty and use Markov decision …

Recently, deep reinforcement learning (DRL) methods have achieved impressive
performance on tasks in a variety of domains. However, neural network policies produced …

Deep reinforcement learning (DRL) has proven capable of superhuman performance on
many complex tasks. To achieve this success, DRL algorithms train a decision-making agent …

We present REVEL, a partially neural reinforcement learning (RL) framework for provably
safe exploration in continuous state and action spaces. A key challenge for provably safe …

Deep learning has transformed the way we think of software and what it can do. But deep
neural networks are fragile and their behaviors are often surprising. In many settings, we …