English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Traditional control methods of robotic peg-in-hole assembly rely on complexcontact state analysis. Reinforcement learning (RL) is gradually becoming apreferred method of controlling robotic peg-in-hole assembly tasks. However,the training process of RL is quite time-consuming because RL methods arealways globally connected, which means all state components are assumed to bethe input of policies for all action components, thus increasing action spaceand state space to be explored. In this paper, we first define continuous spaceserialized Shapley value (CS3) and construct a connection graph to clarify thecorrelativity of action components on state components. Then we propose a localconnection reinforcement learning (LCRL) method based on the connection graph,which eliminates the influence of irrelevant state components on the selectionof action components. The simulation and experiment results demonstrate thatthe control strategy obtained through LCRL method improves the stability andrapidity of the control process. LCRL method will enhance the data-efficiencyand increase the final reward of the training process.

Local Connection Reinforcement Learning Method for Efficient Control of Robotic Peg-in-Hole Assembly