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) aims to learn a policy using only pre-collected and fixed
data. Although avoiding the time-consuming online interactions in RL, it poses challenges …

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 …

It is of significance for an agent to autonomously explore the environment and learn a widely
applicable and general-purpose goal-conditioned policy that can achieve diverse goals …

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

In recent years, reinforcement learning (RL) has been widely used in decision-making.
However, it still faces challenges when it is applied to autonomous driving, especially in …

Reinforcement Learning (RL) with Skill Diversity (SD) is an appealing method of imitating
expert trajectory. Numeral papers have presented methods of SD in several imitation …

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 …