The Arcade Learning Environment (ALE) is a popular platform for evaluating reinforcement
learning agents. Much of the appeal comes from the fact that Atari games demonstrate …

This paper investigates whether learning contingency-awareness and controllable aspects
of an environment can lead to better exploration in reinforcement learning. To investigate …

In this article we introduce the Arcade Learning Environment (ALE): both a challenge
problem and a platform and methodology for evaluating the development of general, domain …

Deep reinforcement learning~(RL) has achieved remarkable successes in complex single-
task settings. However, designing RL agents that can learn multiple tasks and leverage prior …

One practical challenge in reinforcement learning (RL) is how to make quick adaptations
when faced with new environments. In this paper, we propose a principled framework for …

A key challenge in model-based reinforcement learning (RL) is to synthesize
computationally efficient and accurate environment models. We show that carefully …

Abstract We introduce CuLE (CUDA Learning Environment), a CUDA port of the Atari
Learning Environment (ALE) which is used for the development of deep reinforcement …

Much human and computational effort has aimed to improve how deep reinforcement
learning algorithms perform on benchmarks such as the Atari Learning Environment …

Recent progress in Reinforcement Learning (RL), fueled by its combination, with Deep
Learning has enabled impressive results in learning to interact with complex virtual …

Recent reinforcement learning (RL) approaches have shown strong performance in complex
domains such as Atari games, but are often highly sample inefficient. A common approach to …