  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 relationships, traditional knowledge graphs are
static and encyclopedic in nature. On this basis, event knowledge graph (Event
KG) models the temporal and spatial dynamics by text processing to facilitate
downstream applications, such as question-answering, recommendation and
intelligent search. Existing KG research, on the other hand, mostly focuses on
text processing and static facts, ignoring the vast quantity of dynamic
behavioral information included in photos, movies, and pre-trained neural
networks. In addition, no effort has been done to include behavioral
intelligence information into the knowledge graph for deep reinforcement
learning (DRL) and robot learning. In this paper, we propose a novel dynamic
knowledge and skill graph (KSG), and then we develop a basic and specific KSG
based on CN-DBpedia. The nodes are divided into entity and attribute nodes,
with entity nodes containing the agent, environment, and skill (DRL policy or
policy representation), and attribute nodes containing the entity description,
pre-train network, and offline dataset. KSG can search for different agents'
skills in various environments and provide transferable information for
acquiring new skills. This is the first study that we are aware of that looks
into dynamic KSG for skill retrieval and learning. Extensive experimental
results on new skill learning show that KSG boosts new skill learning
efficiency.
