Reinforcement learning (RL) has achieved tremendous success in many complex decision
making tasks. When it comes to deploying RL in the real world, safety concerns are usually …

Safe reinforcement learning (RL) aims to learn policies that satisfy certain constraints before
deploying them to safety-critical applications. Previous primal-dual style approaches suffer …

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 …

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 …

Satisfying safety constraints almost surely (or with probability one) can be critical for the
deployment of Reinforcement Learning (RL) in real-life applications. For example, plane …

While machine learning can myopically reinforce social inequalities, it may also be used to
dynamically seek equitable outcomes. In this paper, we formalize long-term fairness as an …

Safe reinforcement learning aims to learn the optimal policy while satisfying safety
constraints, which is essential in real-world applications. However, current algorithms still …

Reinforcement learning is widely used in applications where one needs to perform
sequential decisions while interacting with the environment. The problem becomes more …

Safety is a critical concern when applying reinforcement learning (RL) to real-world control
tasks. However, existing safe RL works either only consider expected safety constraint …

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 …