The offline reinforcement learning (RL) setting (also known as full batch RL), where a policy
is learned from a static dataset, is compelling as progress enables RL methods to take …

With the widespread adoption of deep learning, reinforcement learning (RL) has
experienced a dramatic increase in popularity, scaling to previously intractable problems …

Reinforcement learning (RL) provides an appealing formalism for learning control policies
from experience. However, the classic active formulation of RL necessitates a lengthy active …

Recent progress in deep learning has relied on access to large and diverse datasets. Such
data-driven progress has been less evident in offline reinforcement learning (RL), because …

Deep reinforcement learning (deep RL) research has grown significantly in recent years. A
number of software offerings now exist that provide stable, comprehensive implementations …

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 …

Offline reinforcement learning methods hold the promise of learning policies from pre-
collected datasets without the need to query the environment for new transitions. This setting …

Natural agents can effectively learn from multiple data sources that differ in size, quality, and
types of measurements. We study this heterogeneity in the context of offline reinforcement …

Offline methods for reinforcement learning have a potential to help bridge the gap between
reinforcement learning research and real-world applications. They make it possible to learn …