A Quantum-inspired Q-learning Algorithm for Indoor Robot Navigation

Abstract

A quantum-inspired Q-learning (QIQL) algorithm is proposed for indoor robot navigation control. Q- learning is an action-dependent reinforcement learning method and has been widely used in robot navigation. Inspired by the fundamental characteristics of quantum computation, e.g. state superposition principle and quantum parallel computation, probability is introduced to Q-learning and along with the learning process the probability of each action to be selected at a certain state is updated, which leads to a natural exploration strategy instead of a pointed one with configured parameters. The simulated navigation experiments show that the proposed QIQL algorithm keeps a good balance of exploration and exploitation, which can avoid the local optimal policies and accelerate the learning process as well.