مشخصات پژوهش

عنوان	Automatic Skill Acquisition in Reinforcement Learning using Connection Graph Stability Centrality
نوع پژوهش	مقاله ارائه شده کنفرانسی
کلیدواژه‌ها	Reinforcement Learning, Skill Acquisition, Option, Hierarchical Reinforcement Learning,
چکیده	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.
پژوهشگران	مارتین هاسلر (نفر سوم)، پرهام مرادی دولت آبادی (نفر دوم)، علی اژدری راد (نفر اول)