Name
Abstract | ||
|---|---|---|
The hierarchical reinforcement learning framework breaks down the reinforcement learning problem into subtasks or extended actions called options in order to facilitate its resolution. Different models have been proposed where options were manually predefined or semi-automatically discovered. However, the automatic discovery of options has become a real challenge for research in hierarchical reinforcement learning and the new proposed approaches are very greedy in learning time or space. Thus we opt for a faster and less consuming approach. In this paper we propose an automatic option discovery method for hierarchical reinforcement learning, that we call FAOD (Fast Automatic Option Discovery). We take inspiration from robot learning methods to categorize the sensorimotor flow during navigation. Here, the agent moves along the walls to discover the rooms' contour, closed spaces, doors and bottleneck regions to define terminate states for options. The FAOD method is evaluated on different classical maze problems, demonstrating fast and promising results. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1142/S0218213021500068 | INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS |
Keywords | DocType | Volume |
Hierarchical reinforcement learning, reinforcement learning, option discovery, Markov decision process | Journal | 30 |
Issue | ISSN | Citations |
2 | 0218-2130 | 0 |
PageRank | References | Authors |
0.34 | 0 | 1 |