Reinforcement Learning Considering Worst Case and Equality within Episodes

Toshihiro Matsui

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Paper

Reinforcement Learning Considering Worst Case and Equality within Episodes

Topics: Agent Models and Architectures; Machine Learning; Planning and Scheduling; State Space Search

In Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, 335-342, 2020 , Valletta, Malta

Author: Toshihiro Matsui

Affiliation: Nagoya Institute of Technology, Gokiso-cho Showa-ku Nagoya 466-8555, Japan

Keyword(s): Reinforcement Learning, Path-finding, Equality, Leximin.

Abstract: Reinforcement learning has been studied as an unsupervised learning framework. The goal of standard reinforcement learning methods is to minimize the total cost or reward for the optimal policy. In several practical situations, equalization of the cost or reward values within an episode may be required. This class of problems can be considered multi-objective, where each part of an episode has individual costs or rewards that should be separately considered. In a previous study this concept was applied to search algorithms for shortest path problems. We investigate how a similar criterion considering the worst-case and equality of the objectives can be applied to the Q-learning method. Our experimental results demonstrate the effect and influence of the optimization with the criterion.

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Paper citation in several formats:

Matsui, T. (2020). Reinforcement Learning Considering Worst Case and Equality within Episodes. In Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART; ISBN 978-989-758-395-7; ISSN 2184-433X, SciTePress, pages 335-342. DOI: 10.5220/0009178603350342

@conference{icaart20,
author={Toshihiro Matsui.},
title={Reinforcement Learning Considering Worst Case and Equality within Episodes},
booktitle={Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART},
year={2020},
pages={335-342},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009178603350342},
isbn={978-989-758-395-7},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART
TI - Reinforcement Learning Considering Worst Case and Equality within Episodes
SN - 978-989-758-395-7
IS - 2184-433X
AU - Matsui, T.
PY - 2020
SP - 335
EP - 342
DO - 10.5220/0009178603350342
PB - SciTePress