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Multi-objective safe reinforcement learning: the relationship between multi-objective reinforcement learning and safe reinforcement learning

  • Naoto Horie
  • Tohgoroh MatsuiEmail author
  • Koichi Moriyama
  • Atsuko Mutoh
  • Nobuhiro Inuzuka
Original Article
  • 2 Downloads

Abstract

Reinforcement learning (RL) is a learning method that learns actions based on trial and error. Recently, multi-objective reinforcement learning (MORL) and safe reinforcement learning (SafeRL) have been studied. The objective of conventional RL is to maximize the expected rewards; however, this may cause a fatal state because safety is not considered. Therefore, RL methods that consider safety during or after learning have been proposed. SafeRL is similar to MORL because it considers two objectives, i.e., maximizing expected rewards and satisfying safety constraints. However, to the best of our knowledge, no study has investigated the relationship between MORL and SafeRL to demonstrate that the SafeRL method can be applied to MORL tasks. This paper combines MORL with SafeRL and proposes a method for Multi-Objective SafeRL (MOSafeRL). We applied the proposed method to resource gathering task, which is a standard task used in MORL test cases.

Keywords

Reinforcement learning Risk control Multi-objective Success probability 

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Copyright information

© International Society of Artificial Life and Robotics (ISAROB) 2019

Authors and Affiliations

  • Naoto Horie
    • 1
    • 3
  • Tohgoroh Matsui
    • 2
    Email author
  • Koichi Moriyama
    • 1
  • Atsuko Mutoh
    • 1
  • Nobuhiro Inuzuka
    • 1
  1. 1.Department of Computer ScienceNagoya Institute of TechnologyNagoyaJapan
  2. 2.Department of Clinical Engineering, College of Life and Health SciencesChubu UniversityKasugaiJapan
  3. 3.Meitetsucom Co. Ltd.NagoyaJapan

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