In this article, we study the design of controllers in the context of stochastic optimal control
under the assumption that the model of the system is not available. This is, we aim to control …

This article deals with distributed policy optimization in reinforcement learning, which
involves a central controller and a group of learners. In particular, two typical settings …

Data Poisoning Attacks (DPA) represent a sophisticated technique aimed at distorting the
training data of machine learning models, thereby manipulating their behavior. This process …

In this paper, we study the learning of safe policies in the setting of reinforcement learning
problems. This is, we aim to control a Markov Decision Process (MDP) of which we do not …

Reinforcement learning is a framework for interactive decision-making with incentives
sequentially revealed across time without a system dynamics model. Due to its scaling to …

Autonomous driving in urban areas is challenging because it requires understanding
vehicle movements, traffic rules, map topologies and unknown environments in the highly …

We study the problem of computing deterministic optimal policies for constrained Markov
decision processes (MDPs) with continuous state and action spaces, which are widely …

Generative Adversarial Imitation Learning (GAIL) stands as a cornerstone approach in
imitation learning. This paper investigates the gradient explosion in two types of GAIL: GAIL …

Reinforcement learning (RL) aims to estimate the action to take given a (time-varying) state,
with the goal of maximizing a cumulative reward function. Predominantly, there are two …

Reinforcement Learning (RL), and these days particularly Deep Reinforcement Learning
(DRL), is concerned with the development, study, and application of algorithms that are …