During initial iterations of training in most Reinforcement Learning (RL) algorithms, agents
perform a significant number of random exploratory steps. In the real world, this can limit the …

Training-time safety violations have been a major concern when we deploy reinforcement
learning algorithms in the real world. This paper explores the possibility of safe RL …

Safety limits the application of traditional reinforcement learning (RL) methods to
autonomous driving. To address the challenge of safe exploration in autonomous driving …

When learning common skills like driving, beginners usually have domain experts standing
by to ensure the safety of the learning process. We formulate such learning scheme under …

This letter aims to solve a safe reinforcement learning (RL) problem with risk measure-based
constraints. As risk measures, such as conditional value at risk (CVaR), focus on the tail …

Reinforcement learning (RL) in low-data and risk-sensitive domains requires performant and
flexible deployment policies that can readily incorporate constraints during deployment. One …

Deep reinforcement learning (DRL) has scored unprecedented success in finding near-
optimal solutions in high-dimensional stochastic problems, leading to its extensive use in …

One of the key challenges to deep reinforcement learning (deep RL) is to ensure safety at
both training and testing phases. In this work, we propose a novel technique of …

Safe exploration is crucial for the real-world application of reinforcement learning (RL).
Previous works consider the safe exploration problem as Constrained Markov Decision …

Integrating learning-based techniques, especially reinforcement learning, into robotics is
promising for solving complex problems in unstructured environments. However, most …