Model-free reinforcement learning (RL) can be used to learn effective policies for complex
tasks, such as Atari games, even from image observations. However, this typically requires …

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 …

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 …

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

Despite significant advances in the field of deep Reinforcement Learning (RL), today's
algorithms still fail to learn human-level policies consistently over a set of diverse tasks such …

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

Intelligent agents need to generalize from past experience to achieve goals in complex
environments. World models facilitate such generalization and allow learning behaviors …

Reinforcement learning has achieved great success in many applications. However, sample
efficiency remains a key challenge, with prominent methods requiring millions (or even …

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

Reinforcement learning and games have a long and mutually beneficial common history.
From one side, games are rich and challenging domains for testing reinforcement learning …