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

Safe reinforcement learning (RL) focuses on training reward-maximizing agents subject to
pre-defined safety constraints. Yet, learning versatile safe policies that can adapt to varying …

This paper considers the problem of learning the reward function and constraints of an
expert from few demonstrations. This problem can be considered as a meta-learning …

Meta-learning has attracted attention due to its strong ability to learn experiences from
known tasks, which can speed up and enhance the learning process for new tasks …

Multi-agent reinforcement learning (MARL) has primarily focused on solving a single task in
isolation, while in practice the environment is often evolving, leaving many related tasks to …

Online safe reinforcement learning (RL) involves training a policy that maximizes task
efficiency while satisfying constraints via interacting with the environments. In this paper, our …

Entropy regularization is an efficient technique for encouraging exploration and preventing a
premature convergence of (vanilla) policy gradient methods in reinforcement learning (RL) …

Operating safely and reliably despite continual distribution shifts is vital for high-stakes
machine learning applications. This paper builds upon the transformative concept …

Recent advances in embedded computing and mobile sensing have led to pervasive use of
robotic systems in both civil and military applications. With single autonomous robots for …

The problem of position estimation using range measurements from transmitters with
inaccurately known positions is considered. The true position of each transmitter is assumed …