Efficiently navigating a superpressure balloon in the stratosphere 1 requires the integration
of a multitude of cues, such as wind speed and solar elevation, and the process is …

The Arcade Learning Environment (ALE) is an evaluation platform that poses the challenge
of building AI agents with general competency across dozens of Atari 2600 games. It …

Abstract Representation learning and option discovery are two of the biggest challenges in
reinforcement learning (RL). Proto-value functions (PVFs) are a well-known approach for …

Reinforcement learning methods trained on few environments rarely learn policies that
generalize to unseen environments. To improve generalization, we incorporate the inherent …

In this paper we introduce a simple approach for exploration in reinforcement learning (RL)
that allows us to develop theoretically justified algorithms in the tabular case but that is also …

… Introducing flavours to the ALE is not one of our contributions, this was done by Machado et
al. (2018). … Marlos C. Machado performed part of this work while at the University of Alberta. …

Options in reinforcement learning allow agents to hierarchically decompose a task into
subtasks, having the potential to speed up learning and planning. However, autonomously …

In this paper, we characterize the behavior of canonical value-based deep reinforcement
learning (RL) approaches under varying degrees of non-stationarity. In particular, we …

The recently introduced Deep Q-Networks (DQN) algorithm has gained attention as one of
the first successful combinations of deep neural networks and reinforcement learning. Its …

The temporal-difference methods TD(λ) and Sarsa(λ) form a core part of modern reinforcement
learning. Their appeal comes from their good performance, low computational cost, and …