Decomposing user-defined tasks in a reinforcement learning setup using TextWorld - Petsanis, Thanos; Keroglou, Christoforos; Ch. Kapoutsis, Athanasios; Kosmatopoulos, Elias B.; Sirakoulis, Georgios Ch. (Frontiers in Robotics and AI, 2023)
The current paper proposes a Hierarchical Reinforcement Learning (HRL) method to decompose a complex task into simpler sub-tasks and leverage those to improve training of an autonomous agent in a simulated environment. For practical reasons (i.e., illustrating purposes, easy implementation, user friendly interface, useful functionalities), we employ two python frameworks called TextWorld and MiniGrid. MiniGrid functions as a 2D simulated representation of the real environment while TextWorld functions as a high-level abstraction of this simulated environment.Training on this abstraction disentangles manipulation from navigation actions and allows us to design a dense reward function instead of a sparse reward function for the lower-level environment which, as we show, improves performance of training. Formal methods are utilized throughout the paper to establish that our algorithm is not prevented to derive to solutions.