Effectively leveraging large, previously collected datasets in reinforcement learn-ing (RL) is
a key challenge for large-scale real-world applications. Offline RL algorithms promise to …

Offline reinforcement learning requires reconciling two conflicting aims: learning a policy that
improves over the behavior policy that collected the dataset, while at the same time …

A compelling use case of offline reinforcement learning (RL) is to obtain a policy initialization
from existing datasets followed by fast online fine-tuning with limited interaction. However …

Offline reinforcement learning (RL) defines the task of learning from a static logged dataset
without continually interacting with the environment. The distribution shift between the …

Offline reinforcement learning (offline RL), which aims to find an optimal policy from a
previously collected static dataset, bears algorithmic difficulties due to function …

Recent works have shown that tackling offline reinforcement learning (RL) with a conditional
policy produces promising results. The Decision Transformer (DT) combines the conditional …

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) promises the ability to learn effective policies solely
using existing, static datasets, without any costly online interaction. To do so, offline RL …

Off-policy reinforcement learning (RL) holds the promise of sample-efficient learning of
decision-making policies by leveraging past experience. However, in the offline RL setting …

Most offline reinforcement learning (RL) methods suffer from the trade-off between improving
the policy to surpass the behavior policy and constraining the policy to limit the deviation …