title: Positive impact of state similarity on reinforcement learning performance.
abstract: In this paper, we propose a novel approach to identify states with similar subpolicies and show how they can be integrated into the reinforcement learning framework to improve learning performance. The method utilizes a specialized tree structure to identify common action sequences of states, which are derived from possible optimal policies, and defines a similarity function between two states based on the number of such sequences. Using this similarity function, updates on the action-value function of a state are reflected onto all similar states. This allows experience that is acquired during learning to be applied to a broader context. The effectiveness of the method is demonstrated empirically.
authors.affiliation: Department of Computer Engineering, Middle East Technical University, 06531 Ankara, Turkey.
authors.forename: Sertan
authors.initials: S
authors.lastname: Girgin
authors.forename: Faruk
authors.initials: F
authors.lastname: Polat
authors.forename: Reda
authors.initials: R
authors.lastname: Alhajj
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D008962:Models, Theoretical; D010363:Pattern Recognition, Automated / Q000379:methods*