Offline reinforcement learning (RL) aims to learn an optimal policy from pre-collected and
labeled datasets, which eliminates the time-consuming data collection in online RL …

This work offers a discussion on how computational mechanics and physics-informed
machine learning can be integrated into the process of sensing, understanding, and …

In this paper, we propose a novel approach called DIffusion-guided DIversity (DIDI) for
offline behavioral generation. The goal of DIDI is to learn a diverse set of skills from a …

As a data-driven paradigm, offline reinforcement learning (RL) has been formulated as
sequence modeling that conditions on the hindsight information including returns, goal or …

The ability to learn continuously is crucial for a robot to achieve a high level of intelligence
and autonomy. In this paper, we consider continual reinforcement learning (RL) for …

Model-based offline Reinforcement Learning (RL) allows agents to fully utilise pre-collected
datasets without requiring additional or unethical explorations. However, applying model …

A prospective application of offline reinforcement learning (RL) involves initializing a pre-
trained policy using existing static datasets for subsequent online fine-tuning. However …

The shape of granular matter (particle) is crucial for understanding their properties and
assembly behavior. Existing studies often rely on intuitive or machine-derived shape …