Variational Quantum Soft Actor-Critic for Robotic Arm Control
- URL: http://arxiv.org/abs/2212.11681v1
- Date: Tue, 20 Dec 2022 19:02:24 GMT
- Title: Variational Quantum Soft Actor-Critic for Robotic Arm Control
- Authors: Alberto Acuto, Paola Barill\`a, Ludovico Bozzolo, Matteo Conterno,
Mattia Pavese, Antonio Policicchio
- Abstract summary: This work aims at exploring and assessing the advantages of the application of Quantum Computing to one of the state-of-art Reinforcement Learning techniques for continuous control.
Specifically, the performance of a Variational Quantum Soft Actor-Critic on the movement of a virtual robotic arm has been investigated by means of digital simulations of quantum circuits.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Deep Reinforcement Learning is emerging as a promising approach for the
continuous control task of robotic arm movement. However, the challenges of
learning robust and versatile control capabilities are still far from being
resolved for real-world applications, mainly because of two common issues of
this learning paradigm: the exploration strategy and the slow learning speed,
sometimes known as "the curse of dimensionality". This work aims at exploring
and assessing the advantages of the application of Quantum Computing to one of
the state-of-art Reinforcement Learning techniques for continuous control -
namely Soft Actor-Critic. Specifically, the performance of a Variational
Quantum Soft Actor-Critic on the movement of a virtual robotic arm has been
investigated by means of digital simulations of quantum circuits. A quantum
advantage over the classical algorithm has been found in terms of a significant
decrease in the amount of required parameters for satisfactory model training,
paving the way for further promising developments.
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