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

Offline reinforcement learning methods hold the promise of learning policies from pre-
collected datasets without the need to query the environment for new samples. This setting …

Offline reinforcement learning methods hold the promise of learning policies from pre-
collected datasets without the need to query the environment for new transitions. This setting …

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

Reinforcement learning (RL) agents can leverage batches of previously collected data to
extract a reasonable control policy. An emerging issue in this offline RL setting, however, is …

The offline reinforcement learning (RL) setting (also known as full batch RL), where a policy
is learned from a static dataset, is compelling as progress enables RL methods to take …

Off-policy reinforcement learning algorithms promise to be applicable in settings where only
a fixed data-set (batch) of environment interactions is available and no new experience can …

Most reinforcement learning (RL) algorithms assume online access to the environment, in
which one may readily interleave updates to the policy with experience collection using that …

Offline reinforcement learning (RL) enables learning control policies by utilizing only prior
experience, without any online interaction. This can allow robots to acquire generalizable …

Many continuous control tasks have easily formulated objectives, yet using them directly as
a reward in reinforcement learning (RL) leads to suboptimal policies. Therefore, many …