Reinforcement learning (RL) methods usually treat reward functions as black boxes. As
such, these methods must extensively interact with the environment in order to discover …

Reward is the driving force for reinforcement-learning agents. This paper is dedicated to
understanding the expressivity of reward as a way to capture tasks that we would want an …

We study the problem of learning control policies for complex tasks given by logical
specifications. Recent approaches automatically generate a reward function from a given …

Stochastic hybrid systems have received significant attentions as a relevant modeling
framework describing many systems, from engineering to the life sciences: they enable the …

The concept of rationality is central to the field of artificial intelligence. Whether we are
seeking to simulate human reasoning, or the goal is to achieve bounded optimality, we …

Reinforcement Learning (RL) has achieved tremendous development in recent years, but
still faces significant obstacles in addressing complex real-life problems due to the issues of …

Goal-conditioned reinforcement learning (RL) is a powerful approach for learning general-
purpose skills by reaching diverse goals. However, it has limitations when it comes to task …

In this paper, we study the expressivity of scalar, Markovian reward functions in
Reinforcement Learning (RL), and identify several limitations to what they can express …

Reinforcement Learning (RL) is a widely employed machine learning architecture that has
been applied to a variety of control problems. However, applications in safety-critical …

The integration of computing and communication capabilities into the power grid has led to
vulnerabilities enabling attackers to launch cyberattacks on the grid. The resources that can …