Offline reinforcement learning (RL) aims at learning effective policies from historical data
without extra environment interactions. During our experience of applying offline RL, we …

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 …

Offline reinforcement learning (RL) aims to learn policy from the passively collected offline
dataset. Applying existing RL methods on the static dataset straightforwardly will raise …

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 …

Despite recent advances in the field of Offline Reinforcement Learning (RL), less attention
has been paid to understanding the behaviors of learned RL agents. As a result, there …

In this work, we argue for the importance of an online evaluation budget for a reliable
comparison of deep offline RL algorithms. First, we delineate that the online evaluation …

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

In offline reinforcement learning (RL), the goal is to learn a highly rewarding policy based
solely on a dataset of historical interactions with the environment. This serves as an extreme …

Offline reinforcement learning (RL) enables learning a decision-making policy without
interaction with the environment. This makes it particularly beneficial in situations where …