Flexibility of Emulation Learning from Pioneers in Nonstationary Environments

DOI

Bibliographic Information

Other Title
  • 非定常環境における先駆者からのエミュレーション学習の柔軟性

Abstract

<p>In imitation learning, the agent observes specific action-state pair sequences of another agent (expert) and somehow reflect them into its own action. One of its implementations in reinforcement learning is the inverse reinforcement learning. We propose a new framework for social learning, emulation learning, which requires much less information from another agent (pioneer). In emulation learning, the agent is given only a certain level of achievement (accumulated rewards per episode). In this study, we implement emulation learning in the reinforcement learning setting by applying a model of satisficing action policy. We show that the emulation learning algorithm works well in a non-stationary reinforcement learning tasks, breaking the often observed trade-off like relationship between optimality and flexibility.</p>

Journal

Details

  • CRID
    1390282752372294272
  • NII Article ID
    130007658419
  • DOI
    10.11517/pjsai.jsai2019.0_2d3e402
  • Text Lang
    en
  • Data Source
    • JaLC
    • CiNii Articles
  • Abstract License Flag
    Disallowed

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