In recent years, the interest in leveraging quantum effects for enhancing machine learning
tasks has significantly increased. Many algorithms speeding up supervised and …

Markov Decision Processes (MDPs) are a mathematical framework for modeling sequential
decision problems under uncertainty as well as reinforcement learning problems. Written by …

We present a general framework for applying machine-learning algorithms to the verification
of Markov decision processes (MDPs). The primary goal of these techniques is to improve …

With the rapid development of electric vehicles (EVs), the dramatic rise in the demand for
electricity is creating heavy pressure on local grids. The combination of renewable energy …

Collaboration requires agents to coordinate their behavior on the fly, sometimes cooperating
to solve a single task together and other times dividing it up into sub‐tasks to work on in …

Unavoidable dead-ends are common in many probabilistic planning problems, eg when
actions may fail or when operating under resource constraints. An important objective in …

Cargo-bearing unmanned aerial vehicles (UAVs) have tremendous potential to assist
humans by delivering food, medicine, and other supplies. For time-critical cargo delivery …

Attaining autonomous flight is an important task in aerial robotics. Often flight trajectories are
not only subject to unknown system dynamics, but also to specific task constraints. This …

State-of-the-art efficient model-based Reinforcement Learning (RL) algorithms typically act
by iteratively solving empirical models, ie, by performing full-planning on Markov Decision …

Abstract Model checking undiscounted reachability and expected-reward properties on
Markov decision processes (MDPs) is key for the verification of systems that act under …