Publication in conference proceedings Q4
Hierarchical reinforcement learning: Learning sub-goals and state-abstraction
Luís Nunes (Nunes, Luis); David Jardim (Jardim, D.); Sancho Moura Oliveira (Oliveira, S.);
6th Iberian Conference on Information Systems and Technologies (CISTI 2011)
Year (definitive publication)
2011
Language
English
Country
United States of America
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Abstract
In this paper we present a method that allows an agent to discover and create temporal abstractions autonomously. Our method is based on the concept that to reach the goal, the agent must pass through relevant states that we will interpret as subgoals. To detect useful subgoals, our method creates intersections between several paths leading to a goal. Our research focused on domains largely used in the study of temporal abstractions. We used several versions of the room-to-room navigation problem. We determined that, in the problems tested, an agent can learn more rapidly by automatically discovering subgoals and creating abstractions.
Acknowledgements
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Keywords
  • Computer and Information Sciences - Natural Sciences