Variational Quantum Soft Actor-Critic for Robotic Arm Control
This work addresses robotic control problems for real-world applications, but it is incremental as it adapts an existing method with quantum computing.
The paper tackles the challenges of exploration and slow learning in deep reinforcement learning for robotic arm control by applying quantum computing to Soft Actor-Critic, resulting in a significant reduction in required parameters for training.
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.