Sample efficiency and exploration remain major challenges in online reinforcement learning
(RL). A powerful approach that can be applied to address these issues is the inclusion of …

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

Two central paradigms have emerged in the reinforcement learning (RL) community: online
RL and offline RL. In the online RL setting, the agent has no prior knowledge of the …

Learning efficiently from small amounts of data has long been the focus of model-based
reinforcement learning, both for the online case when interacting with the environment, and …

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 …

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

CORL is an open-source library that provides thoroughly benchmarked single-file
implementations of both deep offline and offline-to-online reinforcement learning algorithms …

In this paper, we introduce d3rlpy, an open-sourced offline deep reinforcement learning (RL)
library for Python. d3rlpy supports a set of offline deep RL algorithms as well as off-policy …

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

In this tutorial article, we aim to provide the reader with the conceptual tools needed to get
started on research on offline reinforcement learning algorithms: reinforcement learning …