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

We study sequential decision-making problems in which each agent aims to maximize the
expected total reward while satisfying a constraint on the expected total utility. We employ …

Abstract Domain generalization (DG) seeks predictors which perform well on unseen test
distributions by leveraging data drawn from multiple related training distributions or …

In safe reinforcement learning (SRL) problems, an agent explores the environment to
maximize an expected total reward and meanwhile avoids violation of certain constraints on …

Safe exploration presents a major challenge in reinforcement learning (RL): when active
data collection requires deploying partially trained policies, we must ensure that these …

Abstract Machine learning control (MLC) is a highly flexible and adaptable method that
enables the design, modeling, tuning, and maintenance of building controllers to be more …

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 …

We address the issue of safety in reinforcement learning. We pose the problem in an
episodic framework of a constrained Markov decision process. Existing results have shown …

We study the problem of computing an optimal policy of an infinite-horizon discounted
constrained Markov decision process (constrained MDP). Despite the popularity of …

This paper deals with distributed reinforcement learning problems with safety constraints. In
particular, we consider that a team of agents cooperate in a shared environment, where …