Combining offline and online reinforcement learning (RL) is crucial for efficient and safe
learning. However, previous approaches treat offline and online learning as separate …

Offline reinforcement learning algorithms have proven effective on datasets highly
connected to the target downstream task. Yet, leveraging a novel testbed (MOOD) in which …

Offline safe reinforcement learning (RL) aims to train a policy that satisfies constraints using
a pre-collected dataset. Most current methods struggle with the mismatch between imperfect …

Offline Reinforcement Learning (RL) endeavors to leverage offline datasets to craft effective
agent policy without online interaction, which imposes proper conservative constraints with …

In offline reinforcement learning, the challenge of out-of-distribution (OOD) is pronounced.
To address this, existing methods often constrain the learned policy through policy …

Training intelligent agents to navigate highly interactive environments presents significant
challenges. While guided meta reinforcement learning (RL) approach that first trains a …

Offline Reinforcement Learning (RL) has received significant interest due to its ability to
improve policies in previously collected datasets without online interactions. Despite its …

Offline policy learning methods in batch learning aim to derive a policy from a logged bandit
feedback dataset, encompassing context, action, propensity score and feedback for each …

Offline Reinforcement Learning (Offline RL) is challenged by distributional shift and value
overestimation, which often leads to poor performance. To address this issue, a popular …