Reinforcement learning has shown promising results in learning neural network policies for
complicated control tasks. However, the lack of formal guarantees about the behavior of …

Markov chain analysis is a key technique in formal verification. A practical obstacle is that all
probabilities in Markov models need to be known. However, system quantities such as …

This article presents a neurosymbolic framework to solve motion planning problems for
mobile robots involving temporal goals. The temporal goals are described using temporal …

The de-facto standard approach in MDP verification is based on value iteration (VI). We
propose compositional VI, a framework for model checking compositional MDPs, that …

Reinforcement Learning (RL) has become an increasingly important research area as the
success of machine learning algorithms and methods grows. To combat the safety concerns …

Computing schedulers that optimize reachability probabilities in MDPs is a standard
verification task. To address scalability concerns, we focus on MDPs that are compositionally …

This paper presents a new approach to design verified compositions of Neural Network (NN)
controllers for autonomous systems with tasks captured by Linear Temporal Logic (LTL) …

Markov chain analysis is a key technique in formal verification. A practical obstacle is that all
probabilities in Markov models need to be known. However, system quantities such as …

We propose and demonstrate a compositional framework for training and verifying
reinforcement learning (RL) systems within a multifidelity sim-to-real pipeline, in order to …

We propose a compositional approach to synthesize policies for networks of continuous-
space stochastic control systems with unknown dynamics using model-free reinforcement …