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

Offline reinforcement learning (RL) algorithms can acquire effective policies by utilizing only
previously collected experience, without any online interaction. While it is widely understood …

Policy constraint methods to offline reinforcement learning (RL) typically utilize
parameterization or regularization that constrains the policy to perform actions within the …

Offline reinforcement learning (RL) algorithms can acquire effective policies by utilizing
previously collected experience, without any online interaction. It is widely understood that …

Despite the success of Random Network Distillation (RND) in various domains, it was shown
as not discriminative enough to be used as an uncertainty estimator for penalizing out-of …

Recent years have witnessed significant advancements in offline reinforcement learning
(RL), resulting in the development of numerous algorithms with varying degrees of …

Offline imitation learning (IL) promises the ability to learn performant policies from pre-
collected demonstrations without interactions with the environment. However, imitating …

With the fast improvement of machine learning, reinforcement learning (RL) has been used
to automate human tasks in different areas. However, training such agents is difficult and …

Training large neural networks is known to be time-consuming, with the learning duration
taking days or even weeks. To address this problem, large-batch optimization was …

Offline reinforcement learning algorithms still lack trust in practice due to the risk that the
learned policy performs worse than the original policy that generated the dataset or behaves …