In this paper, we investigate the optimal online configuration of episodic Markov decision
processes when the space of the possible configurations is continuous. Specifically, we …

Abstract Probabilistic Answer Set Programming offers an intuitive and powerful declarative
language to represent uncertainty about combinatorial structures. Remarkably, under the …

Solving a decision theory problem usually involves finding the actions, among a set of
possible ones, which optimize the expected reward, while possibly accounting for the …

In recent decades, Reinforcement Learning (RL) has emerged as an effective approach to
address complex control tasks. In a Markov Decision Process (MDP), the framework typically …

Deep Learning has achieved remarkable success in a range of complex perception tasks,
games, and other real-world applications. At a high level, it can be argued that the main …

In this paper, we investigate the optimal online configuration of episodic Markov decision
processes when the space of the possible configurations is continuous. Specifically, we …

Machine Learning (ML) is the field of Artificial Intelligence aimed at the development of
algorithms that learn to perform specific tasks from data. These algorithms aim at increasing …