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 …

Reinforcement learning (RL) has proven its worth in a series of artificial domains, and is
beginning to show some successes in real-world scenarios. However, much of the research …

Reinforcement Learning (RL) algorithms have had tremendous success in simulated
domains. These algorithms, however, often cannot be directly applied to physical systems …

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 …

Reinforcement learning (RL) has proven its worth in a series of artificial domains, and is
beginning to show some successes in real-world scenarios. However, much of the research …

We study the problem of safe offline reinforcement learning (RL), the goal is to learn a policy
that maximizes long-term reward while satisfying safety constraints given only offline data …

We study sequential decision making problems aimed at maximizing the expected total
reward while satisfying a constraint on the expected total utility. We employ the natural policy …

Many physical systems have underlying safety considerations that require that the policy
employed ensures the satisfaction of a set of constraints. The analytical formulation usually …

Reinforcement learning (RL) is a control approach that can handle nonlinear stochastic
optimal control problems. However, despite the promise exhibited, RL has yet to see marked …