Many real-world systems problems require reasoning about the long term consequences of
actions taken to configure and manage the system. These problems with delayed and often …

Reinforcement learning (RL) applications, where an agent can simply learn optimal
behaviors by interacting with the environment, are quickly gaining tremendous success in a …

Abstract Deep Reinforcement Learning (DRL) algorithms have been successfully applied to
a range of challenging control tasks. However, these methods typically suffer from three core …

Despite significant progress in challenging problems across various domains, applying state-
of-the-art deep reinforcement learning (RL) algorithms remains challenging due to their …

In recent years, significant progress has been made in solving challenging problems across
various domains using deep reinforcement learning (RL). Reproducing existing work and …

Abstract Deep Reinforcement Learning (DRL) algorithms have been successfully applied to
a range of challenging control tasks. However, these methods typically suffer from three core …

In computer vision and natural language processing, innovations in model architecture that
lead to increases in model capacity have reliably translated into gains in performance. In …

Model-based reinforcement learning (RL) is considered to be a promising approach to
reduce the sample complexity that hinders model-free RL. However, the theoretical …

Most reinforcement learning (RL) algorithms assume online access to the environment, in
which one may readily interleave updates to the policy with experience collection using that …

Deep Reinforcement Learning (DRL) combines the power of Deep Leaning and
Reinforcement learning, and has started gaining a lot of attraction in various domains. Also …