Authors:
Ignacio Fidalgo
;
Guillermo Villate
;
Alberto Tellaeche
and
Juan Vázquez
Affiliation:
Computer Science, Electronics and Communication Technologies, University of Deusto, Avenida de las Universidades 24, Bilbao, Spain
Keyword(s):
Intelligent Robotics, Reinforcement Learning, Hyperparameters.
Abstract:
Artificial intelligence (AI) is increasingly present in industrial applications and, in particular, in advanced robotics, both industrial and mobile. The main problem of these type of applications is that they use complex AI algorithms, in which it is necessary to establish numerous hyperparameters to achieve an effective training of the same. In this research, we introduce a pioneering approach to reinforcement learning in the realm of industrial robotics, specifically targeting the UR3 robot. By integrating advanced techniques like Deep Q-Learning and Proximal Policy Optimization, we’ve crafted a unique motion planning framework. A standout novelty lies in our application of the Optuna library for hyperparameter optimization, which, while not necessarily enhancing the robot’s end performance, significantly accelerates the convergence to the optimal policy. This swift convergence, combined with our comprehensive analysis of hyperparameters, not only streamlines the training process
but also paves the way for efficient real-world robotic applications. Our work represents a blend of theoretical insights and practical tools, offering a fresh perspective in the dynamic field of robotics.
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