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

Lagrangian methods are widely used algorithms for constrained optimization problems, but
their learning dynamics exhibit oscillations and overshoot which, when applied to safe …

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 …

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 …

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 …

Ride-hailing platforms (eg, Uber and Didi Chuxing) have become increasingly popular in
recent years. Efficiency has always been an important metric for such platforms. However …

In many sequential decision-making problems, the goal is to optimize a utility function while
satisfying a set of constraints on different utilities. This learning problem is formalized …

Safe reinforcement learning (RL) studies problems where an intelligent agent has to not only
maximize reward but also avoid exploring unsafe areas. In this study, we propose CUP, a …

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