Local Roots A Tree Based Subgoal Discovery Method to Accelerate Reinforcement Learning

Date

2016-12-04

Author

Demir, Alper
Polat, Faruk
Cilden, Erkin

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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Subgoal discovery in reinforcement learning is an effective way of partitioning a problem domain with large state space. Recent research mainly focuses on automatic identification of such subgoals during learning, making use of state transition information gathered during exploration. Mostly based on the options framework, an identified subgoal leads the learning agent to an intermediate region which is known to be useful on the way to goal. In this paper, we propose a novel automatic subgoal discovery method which is based on analysis of predicted shortcut history segments derived from experience, which are then used to generate useful options to speed up learning. Compared to similar existing methods, it performs significantly better in terms of time complexity and usefulness of the subgoals identified, without sacrificing solution quality. The effectiveness of the method is empirically shown via experimentation on various benchmark problems compared to well known subgoal identification methods.

Subject Keywords

Abstraction in reinforcement learning, Subgoal discovery, Options framework

URI

https://hdl.handle.net/11511/68833

DOI

https://doi.org/10.1007/978-3-319-46227-1_23

Collections

Department of Computer Engineering, Conference / Seminar

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Citation Formats

A. Demir, F. Polat, and E. Cilden, “Local Roots A Tree Based Subgoal Discovery Method to Accelerate Reinforcement Learning,” 2016, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/68833.