Transformers, originally devised for natural language processing (NLP), have also produced
significant successes in computer vision (CV). Due to their strong expression power …

Foundation models (FMs), large deep learning models pre-trained on vast, unlabeled
datasets, exhibit powerful capabilities in understanding complex patterns and generating …

Recent advancements in offline reinforcement learning (RL) have underscored the
capabilities of Conditional Sequence Modeling (CSM), a paradigm that learns the action …

The purpose of offline multi-task reinforcement learning (MTRL) is to develop a unified policy
applicable to diverse tasks without the need for online environmental interaction. Recent …

A longstanding goal of artificial general intelligence is highly capable generalists that can
learn from diverse experiences and generalize to unseen tasks. The language and vision …

Offline reinforcement learning (RL) algorithms can learn better decision-making compared to
behavior policies by stitching the suboptimal trajectories to derive more optimal ones …

Decision Transformer (DT) has emerged as a promising class of algorithms in offline
reinforcement learning (RL) tasks, leveraging pre-collected datasets and Transformer's …

Transformer-based trajectory optimization methods have demonstrated exceptional
performance in offline Reinforcement Learning (offline RL), yet it poses challenges due to …

The purpose of offline multi-task reinforcement learning (MTRL) is to develop a unified policy
applicable to diverse tasks without the need for online environmental interaction. Recent …

Decision transformers recast reinforcement learning as a conditional sequence generation
problem, offering a simple but effective alternative to traditional value or policy-based …