Modern detection transformers (DETRs) use a set of object queries to predict a list of
bounding boxes, sort them by their classification confidence scores, and select the top …

Offline-to-online reinforcement learning (RL) is a training paradigm that combines pre-
training on a pre-collected dataset with fine-tuning in an online environment. However, the …

The divergence of the Q-value estimation has been a prominent issue offline reinforcement
learning (offline RL), where the agent has no access to real dynamics. Traditional beliefs …

In applying reinforcement learning (RL) to high-stakes domains, quantitative and qualitative
evaluation using observational data can help practitioners understand the generalization …

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 …