2024 : 2 : 27
Parham Moradi

Parham Moradi

Academic rank: Associate Professor
Education: PhD.
ScopusId: 654
Faculty: Faculty of Engineering
Address: Department of Computer Engineering, Faculty of Engineering, University of Kurdistan


Automatic Skill Acquisition in Reinforcement Learning using Connection Graph Stability Centrality
Reinforcement Learning, Skill Acquisition, Option, Hierarchical Reinforcement Learning,
Researchers Martin Hasler ، Parham Moradi ، Ali Ajdarirad


Reinforcement Learning (RL) is an approach for training agent’s behavior through trial-and-error interactions with a dynamic environment. An important problem of RL is that in large domains an enormous number of decisions are to be made. Hence, instead of learning using individual primitive actions, an agent could learn much faster if it could form high level behaviors known as skills. Graph-based approach, that maps the RL problem to a graph, is one of the several approaches proposed to identify the skills to learn automatically. In this paper we propose a new centrality measure for identifying bottleneck nodes crucial to develop useful skills. We will show through simulations for two benchmark tasks, namely, “two-room grid” and “taxi driver” that a procedure based on the proposed measure performs better than the procedure based on closeness and node betweenness centrality.