A large set of the explainable Artificial Intelligence (XAI) literature is emerging on feature
relevance techniques to explain a deep neural network (DNN) output or explaining models …

Reinforcement Learning (RL) is a popular machine learning paradigm where intelligent
agents interact with the environment to fulfill a long-term goal. Driven by the resurgence of …

A major goal of artificial intelligence (AI) is to create an agent capable of acquiring a general
understanding of the world. Such an agent would require the ability to continually …

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 …

Although deep reinforcement learning has become a promising machine learning approach
for sequential decision-making problems, it is still not mature enough for high-stake domains …

Building autonomous machines that can explore open-ended environments, discover
possible interactions and build repertoires of skills is a general objective of artificial …

In many real-world applications, reinforcement learning (RL) agents might have to solve
multiple tasks, each one typically modeled via a reward function. If reward functions are …

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 …

The number of states in a dynamic process is exponential in the number of objects, making
reinforcement learning (RL) difficult in complex, multi-object domains. For agents to scale to …

In recent years, Artificial Intelligence (AI) systems have surpassed human intelligence in a
variety of computational tasks. However, AI systems, like humans, make mistakes, have …