Learning efficiently a causal model of the environment is a key challenge of model-based
RL agents operating in POMDPs. We consider here a scenario where the learning agent …

Commonly in reinforcement learning (RL), rewards are discounted over time using an
exponential function to model time preference, thereby bounding the expected long-term …

Despite recent advances in Reinforcement Learning (RL), the Markov Decision Processes
(MDPs) are not always the best choice to model complex dynamical systems requiring …

Problems concerning dynamic learning and decision making are difficult to solve
analytically. We study an infinite-horizon discrete-time model with a constant unknown state …

In the presence of confounding, naively using off-the-shelf offline reinforcement learning
(RL) algorithms leads to sub-optimal behaviour. In this work, we propose a safe method to …

For many quantum systems intended for information processing, one detects the logical
state of a qubit by integrating a continuously observed quantity over time. For example, ion …

This paper presents a generalized graphical outline of the current state of the art in the
development of Markov decision processes (MDPs). This systematization opens ways to …

The availability of vast amounts of electronic medical records data has inspired an
increasing interest in data-driven healthcare intervention planning methods. Disease …

A key problem for many industrial processes is to limit exposure to system malfunction. The
system health state can be represented by different models. However, it is often the case that …

Despite recent advances in Reinforcement Learning (RL), the Markov Decision Processes
are not always the best choice to model complex dynamical systems requiring interactions at …