Controlling an Autonomous Agent for Exploring Unknown Environments Using Switching Prelearned Modules

Takahito Hata, Masanori Suganuma, Tomoharu Nagao

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


In this paper, we try to acquire various behavior patterns of autonomous exploration agent using several learning environments. In case of previous learning methods using a single behavior rule set, it is hard to acquire the behavior that covers all learning environments. In our method, we divide learning environments into some primitive environments whose properties differ each other, and then generate modules that are specialized for each primitive environment. To optimize behavior rules of agents, we adopt graph structured program evolution (GRAPE) which can automatically generate graph structured programs. In unknown environments, each module is switched by a program named “switcher”. The switcher selects the module that acts better in a neighboring environment. Through several experiments, our method achieved higher exploration rate in unknown environments compared to simple GRAPE, random search, and the method that switches modules randomly.

Original languageEnglish
Pages (from-to)84-93
Number of pages10
JournalElectronics and Communications in Japan
Issue number5
Publication statusPublished - 2018 May
Externally publishedYes


  • automatic programming
  • autonomous agent
  • generalization
  • genetic programming
  • modularization

ASJC Scopus subject areas

  • Signal Processing
  • Physics and Astronomy(all)
  • Computer Networks and Communications
  • Electrical and Electronic Engineering
  • Applied Mathematics


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