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 …

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 …

Offline reinforcement learning (RL purely from logged data) is an important avenue for
deploying RL techniques in real-world scenarios. However, existing hyperparameter …

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

Offline reinforcement learning (RL) can be used to improve future performance by
leveraging historical data. There exist many different algorithms for offline RL, and it is well …

Offline reinforcement learning (RL) defines the task of learning from a fixed batch of data.
Due to errors in value estimation from out-of-distribution actions, most offline RL algorithms …

Offline Reinforcement Learning (ORL) enablesus to separately study the two interlinked
processes of reinforcement learning: collecting informative experience and inferring optimal …

Offline reinforcement learning (RL) algorithms have shown promising results in domains
where abundant pre-collected data is available. However, prior methods focus on solving …

We present a novel observation about the behavior of offline reinforcement learning (RL)
algorithms: on many benchmark datasets, offline RL can produce well-performing and safe …

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 …