Representations are at the core of all deep reinforcement learning (RL) methods for both
Markov decision processes (MDPs) and partially observable Markov decision processes …

Highway merging scenarios featuring mixed traffic conditions pose significant modeling and
control challenges for connected and automated vehicles (CAVs) interacting with incoming …

In this paper, we investigate discrete-time decision-making problems in uncertain systems
with partially observed states. We consider a non-stochastic model, where uncontrolled …

We study the task of learning state representations from potentially high-dimensional
observations, with the goal of controlling an unknown partially observable system. We …

The traditional approach to POMDPs is to convert them into fully observed MDPs by
considering a belief state as an information state. However, a belief-state based approach …

A common approach to solve partially observable Markov decision processes (POMDPs) is
transforming them into Makov decision processes (MDPs) defined on information states …

We present an approach for systematically anticipating the actions and policies employed
by\emph {oblivious} environments in concurrent stochastic games, while maximizing a …

Reinforcement learning (RL) for partially observable Markov decision processes (POMDPs)
is a challenging problem because decisions need to be made based on the entire history of …

The advent of cyber-physical systems has revolutionized numerous applications, including
connected and automated vehicles, medicine and healthcare, the Internet of Things, social …

Within the context of decision-making, risk is a measure of potential harm to the decision-
maker's desired outcome, arising from the uncertainties associated with the likelihood of …