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 …

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 …

We study the problem of learning control policies for complex tasks given by logical
specifications. Recent approaches automatically generate a reward function from a given …

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 …

Programs offer compactness and structure that makes them an attractive representation for
visual data. We explore how code rewriting can be used to improve systems for inferring …

Deep reinforcement learning (RL) has led to encouraging successes in many challenging
control tasks. However, a deep RL model lacks interpretability due to the difficulty of …