Transformer-Enhanced Actor-Critic Reinforcement Learning for Simulation-Based Autonomous Spot-Welding Control of Robotic Manipulators

2026

Full text Open access: Gold Metadata (CSL-JSON)
DOI 10.32604/cmc.2026.088246
Data confidence 70 medium
How this score is calculated

It is built from checkable properties of the record itself: whether the article has a DOI, how many independent sources confirmed its data, whether the required fields are filled in, and how recent the latest confirmation is. Disagreement between sources and a missing identifier lower it. A value of 80 or above means the record was published without further checking; 60 to 79 means it is published at reduced confidence, with a manual check advisable. A record verified by an editor by hand is never pushed below 90 by a recalculation. This scores our data about the article, not the article itself.

Abstract

This study presents a Transformer-Enhanced Actor-Critic Reinforcement Learning framework for simulation-based control of sequential robotic spot-welding tasks. The method couples a shared temporal encoder with deterministic a... | Find, read and cite all the research you need on Tech Science Press