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ORIGINAL RESEARCH article

Front. Robot. AI

Sec. Robot Learning and Evolution

Volume 12 - 2025 | doi: 10.3389/frobt.2025.1567211

Comparative Analysis of Deep Q-learning Algorithms for Throwing Objects Using a Robot Manipulator

Provisionally accepted
  • 1University of Palermo, Palermo, Italy
  • 2School of Electrical Engineering, University of Belgrade, Belgrade, Serbia
  • 3University of Picardie Jules Verne, Amiens, Picardy, France

The final, formatted version of the article will be published soon.

Recent advances in artificial intelligence have attracted significant attention due to AI's ability to solve complex problems and the rapid development of learning algorithms and computational power. Among the many AI techniques, transformers stand out for their flexible architectures and high computational capacity. Unlike traditional neural networks, transformers use mechanisms such as self-attention with positional encoding, which enable them to effectively capture long-range dependencies in sequential and spatial data. This paper presents a comparison of various deep Q-learning algorithms and proposes two original techniques that use self-attention into deep Q-learning. The first technique is Structured Self-Attention with deep Q-Learning, and the second uses Multi-Head Attention with deep Q-Learning. These methods are compared with different types of deep Q-Learning and other temporal techniques in uncertain tasks such as throwing objects to unknown targets. The performance of these algorithms is evaluated in a simplified environment, where the task involves throwing a ball using a robotic arm manipulator. This setup provides a controlled scenario to analyze the algorithms' efficiency and effectiveness in solving dynamic control problems. Additional constraints are introduced to evaluate performance under more complex conditions, such as a joint lock or the presence of obstacles like a wall nearby a robot or a target. The output of the algorithm includes the correct joint configurations and trajectories for throwing to unknown target positions. The use of multi-head attention has enhanced the robot's ability to prioritize and interact with critical environmental features. The paper also includes a comparison of temporal difference algorithms to address constraints on the robot's joints. These algorithms are capable of finding solutions within the limitations of existing hardware, enabling robots to interact intelligently and autonomously with their environment.

Keywords: artificial intelligence, deep learning, reinforcement learning, deep Q Learning, robotic manipulation, Object Throwing, Robotics, Self-attention mechanism

Received: 26 Jan 2025; Accepted: 24 Sep 2025.

Copyright: © 2025 Al Homsi, Trumic, Fagiolini and Cirrincione. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Mohammad Al Homsi, mohammad.alhomsi@unipa.it

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.