Reinforcement Learning (RL) has achieved tremendous success in many complex decision-
making tasks. However, safety concerns are raised during deploying RL in real-world …

The last half decade has seen a steep rise in the number of contributions on safe learning
methods for real-world robotic deployments from both the control and reinforcement learning …

Decision-making in the face of uncertainty is a significant challenge in machine learning,
and the multi-armed bandit model is a commonly used framework to address it. This …

Reinforcement learning is a powerful paradigm for learning optimal policies from
experimental data. However, to find optimal policies, most reinforcement learning algorithms …

This tutorial introduces the reader to Gaussian process regression as an expressive tool to
model, actively explore and exploit unknown functions. Gaussian process regression is a …

Modern nonlinear control theory seeks to endow systems with properties of stability and
safety, and have been deployed successfully in multiple domains. Despite this success …

Safe reinforcement learning has been a promising approach for optimizing the policy of an
agent that operates in safety-critical applications. In this paper, we propose an algorithm …

Abstract Recommender Systems (RSs) have assumed a crucial role in several digital
companies by directly affecting their key performance indicators. Nowadays, in this era of big …

We study the safe reinforcement learning problem using the constrained Markov decision
processes in which an agent aims to maximize the expected total reward subject to a safety …

One of the most fundamental problems when designing controllers for dynamic systems is
the tuning of the controller parameters. Typically, a model of the system is used to obtain an …