Autonomous driving has achieved significant milestones in research and development over
the last two decades. There is increasing interest in the field as the deployment of …

In this tutorial article, we aim to provide the reader with the conceptual tools needed to get
started on research on offline reinforcement learning algorithms: reinforcement learning …

While deep reinforcement learning (RL) has fueled multiple high-profile successes in
machine learning, it is held back from more widespread adoption by its often poor data …

Recent work has considered whether large language models (LLMs) can function as
planners: given a task, generate a plan. We investigate whether LLMs can serve as …

In reinforcement learning (RL), representation learning is a proven tool for complex image-
based tasks, but is often overlooked for environments with low-level states, such as physical …

In this article, we aim to provide a literature review of different formulations and approaches
to continual reinforcement learning (RL), also known as lifelong or non-stationary RL. We …

In lifelong learning, the learner is presented with a sequence of tasks, incrementally building
a data-driven prior which may be leveraged to speed up learning of a new task. In this work …

Hierarchical reinforcement learning (HRL) is a promising approach to extend traditional
reinforcement learning (RL) methods to solve more complex tasks. Yet, the majority of …

Deep reinforcement learning (RL) has achieved outstanding results in recent years. This has
led to a dramatic increase in the number of applications and methods. Recent works have …

In this paper we argue for the fundamental importance of the value distribution: the
distribution of the random return received by a reinforcement learning agent. This is in …