Placement is a critical step in modern chip design, aiming to determine the positions of
circuit modules on the chip canvas. Recent works have shown that reinforcement learning …

Offline reinforcement learning (RL) is a challenging setting where existing off-policy actor-
critic methods perform poorly due to the overestimation of out-of-distribution state-action …

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 …

Unsupervised reinforcement learning aims to acquire skills without prior goal
representations, where an agent automatically explores an open-ended environment to …

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 …

The recent development of industrial manufacturing and social services has witnessed a
significant trend of automation and intelligentization due to the wide application of robots …

The fundamental concept behind option-based hierarchical reinforcement learning (O-HRL)
is to obtain temporal coarse-grained actions and abstract complex situations. Although O …

Humans can perform infinite diverse skills. These skills typically represent abstract
knowledge that is highly correlated with time series. To behave more like a human, we take …

When it comes to the control system of quadruped robots, deep reinforcement learning
(DRL) is considered to be a promising solution. Despite years of development in this field …

The knowledge graph (KG) is an essential form of knowledge representation that has grown
in prominence in recent years. Because it concentrates on nominal entities and their …