An Intelligent IoT-Driven Smart Shopping Cart with Reinforcement Learning for Optimized Store Navigation

In the retail commercial sector, various advancements in IoT and AI have industrially renovated the domain. Due to the introduction of boosted connectivity and availability of intelligent systems pave way for redefined shopping experience. The current research work highlights a pioneering tactic to improve shopping experiences. It integrates an intelligent IoT-driven Perceptive shopping cart and reinforcement learning techniques for optimized store navigation. In the research work, the salient features of the two existing deep learning algorithms viz. Reinforcement Learning and Q Network with Long Short-Term Memory are integrated and an innovative algorithm called Reinforced Q-Network with Long Short-Term Memory (ReQL-Net) is proposed. The ReQL-Net algorithm authorizes the perceptive cart to unconventionally acquire effectual navigation strategies. Extensive simulation results show that The ReQL-Net algorithm outperforms other reinforcement learning or Q-Network methods in terms of Navigation Efficiency, Exploitation Trade-off and Convergence Rate. Using the proposed algorithm, the customer can personalize his shopping experience. Also the shopkeeper can designed for individual customer experiences. This information can be used to create targeted marketing strategies. Also, such researches on smart shopping cart opens up new possibilities for the future of intelligent shopping systems which can adapt to changing retail environments.