Multi-task offline reinforcement learning aims to develop a unified policy for diverse tasks
without requiring real-time interaction with the environment. Recent work explores sequence …

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 …

Diffusion models have demonstrated their capabilities in modeling trajectories of multi-tasks.
However, existing multi-task planners or policies typically rely on task-specific …

Offline reinforcement learning (RL) methods harness previous experiences to derive an
optimal policy, forming the foundation for pre-trained large-scale models (PLMs). When …

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 …

In numerous artificial intelligence applications, the collaborative efforts of multiple intelligent
agents are imperative for the successful attainment of target objectives. To enhance …

A significant aspiration of offline reinforcement learning (RL) is to develop a generalist agent
with high capabilities from large and heterogeneous datasets. However, prior approaches …

Curiosity is a cognitive mechanism that drives one's intrinsic need to understand the
unknown. This intrinsic drive is responsible for guiding the acquisition of knowledge about …