Width-Based Algorithms for Common Problems in Control, Planning and Reinforcement Learning
Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence | International Joint Conferences on Artificial Intelligence Organization | Published : 2021
Width-based algorithms search for solutions through a general definition of state novelty. These algorithms have been shown to result in state-of-the-art performance in classical planning, and have been successfully applied to model-based and model-free settings where the dynamics of the problem are given through simulation engines. Width-based algorithms performance is understood theoretically through the notion of planning width, providing polynomial guarantees on their runtime and memory consumption. To facilitate synergies across research communities, this paper summarizes the area of width-based planning, and surveys current and future research directions.