Generalisation in Multitask Fitted Q-Iteration and Offline Q-learning

This article likely explores the generalization capabilities of Q-learning algorithms, specifically in multitask and offline settings. The focus is on how these algorithms perform when applied to new, unseen tasks or data. The research probably investigates the factors that influence generalization, such as the choice of function approximators, the structure of the tasks, and the amount of available data. The use of 'Fitted Q-Iteration' suggests a focus on batch reinforcement learning, where the agent learns from a fixed dataset.