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 …

To solve complex real-world problems with reinforcement learning, we cannot rely on
manually specified reward functions. Instead, we need humans to communicate an objective …

Achieving efficient and scalable exploration in complex domains poses a major challenge in
reinforcement learning. While Bayesian and PAC-MDP approaches to the exploration …

Deep reinforcement learning agents have achieved state-of-the-art results by directly
maximising cumulative reward. However, environments contain a much wider variety of …

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 …

Atari games have been a long-standing benchmark in the reinforcement learning (RL)
community for the past decade. This benchmark was proposed to test general competency …

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 …

Recently, various auxiliary tasks have been proposed to accelerate representation learning
and improve sample efficiency in deep reinforcement learning (RL). However, existing …

We introduce the first deep reinforcement learning agent that learns to beat Atari games with
the aid of natural language instructions. The agent uses a multimodal embedding between …

To be successful in real-world tasks, Reinforcement Learning (RL) needs to exploit the
compositional, relational, and hierarchical structure of the world, and learn to transfer it to the …