Automation is increasingly at the forefront of transportation research, with the potential to
bring fully autonomous vehicles to our roads in the coming years. This comprehensive …

This position paper reflects on the state-of-the-art in decision-making under uncertainty. A
classical assumption is that probabilities can sufficiently capture all uncertainty in a system …

Much of reinforcement learning theory is built on top of oracles that are computationally hard
to implement. Specifically for learning near-optimal policies in Partially Observable Markov …

Markov decision processes (MDPs) are formal models commonly used in sequential
decision-making. MDPs capture the stochasticity that may arise, for instance, from imprecise …

Safe exploration is a common problem in reinforcement learning (RL) that aims to prevent
agents from making disastrous decisions while exploring their environment. A family of …

We study safe policy improvement (SPI) for partially observable Markov decision processes
(POMDPs). SPI is an offline reinforcement learning (RL) problem that assumes access to (1) …

We present a novel learning framework to obtain finite-state controllers (FSCs) for partially
observable Markov decision processes and illustrate its applicability for indefinite-horizon …

This paper marries two state-of-the-art controller synthesis methods for partially observable
Markov decision processes (POMDPs), a prominent model in sequential decision making …

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 …

Partially observable Markov decision processes (POMDPs) are models for sequential
decision-making under uncertainty and incomplete information. Machine learning methods …