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) 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 (RL), also known as batch RL, offers the prospect of policy
optimization from large pre-recorded datasets without online environment interaction. It …

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 …

Off-policy reinforcement learning (RL) using a fixed offline dataset of logged interactions is
an important consideration in real world applications. This paper studies offline RL using the …

Despite the great empirical success of deep reinforcement learning, its theoretical
foundation is less well understood. In this work, we make the first attempt to theoretically …

Most prior approaches to offline reinforcement learning (RL) have taken an iterative actor-
critic approach involving off-policy evaluation. In this paper we show that simply doing one …

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), a technology that offline learns a policy from logged data
without the need to interact with online environments, has become a favorable choice in …

Reinforcement learning (RL) can be used to learn treatment policies and aid decision
making in healthcare. However, given the need for generalization over complex state/action …