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Conference Object Citation - Scopus: 1A Novel Use of Reinforcement Learning for Elevated Click-Through Rate in Online Advertising(Ieee Computer Soc, 2023) Haider, Umair; Yildiz, BeytullahEfficiently predicting Click-through Rate (CTR) is crucial for the success of online advertising. Traditional methods often struggle to adapt to the dynamic nature of user preferences and the evolving relevance of advertisements. In this study, we propose a novel Reinforcement Learning (RL) approach for CTR prediction, leveraging OpenAI Gym and the Thompson Sampling algorithm. Our approach dynamically estimates CTR, cleverly adapting to the ever-changing landscape of user preferences and advertisement relevance. Results showcase the exceptional performance of Thompson Sampling in CTR prediction, sur-passing other RL methods with a remarkable 10% higher confidence level. This emphasizes the significant potential of our RL approach in optimizing the selection of online advertisements.Conference Object Reinforcement Learning-Based Multi-Robot Path Planning and Congestion Management in Warehouse Order Picking(Institute of Electrical and Electronics Engineers Inc., 2024) Alam, M.S.; Khan, M.U.; Gunes, A.This paper addresses the multi-robot path planning problem in a warehouse environment using reinforcement learning. The warehouse layout comprises of a grid map with multiple robots for retrieval and delivery of orders, inventory pods for storage, and pick stations for receiving outbound orders. The robots are required to pick and deliver orders from target shelves to their corresponding pick stations by navigating in a complex network of aisles. Q-learning algorithm computes optimal paths for the robots, while avoiding congestion in the aisles. Simulation results demonstrate the efficacy of the proposed method in optimizing both travel time and travel distance, thus enhancing the overall operational efficiency of the warehouse. © 2024 IEEE.
