On/off-policy Hybrid Deep Reinforcement Learning and Simulation in Control Tasks

DOI

Bibliographic Information

Other Title
  • On/off-policyのハイブリッド深層強化学習とシミュレーション環境での制御問題への応用

Description

<p>Recently, deep reinforcement learning with neural network shows great performance in tasks such as game AI and robotics control tasks. However, on-policy and off-policy reinforcement learning methods proposed in related works have problems such as slow exploration speed. To solve these problems, we propose a hybrid deep reinforcement learning method which combines on-policy and off-policy reinforcement learning in this paper. The comparison experiment shows that the proposed method outperforms classic DDPG and DPPO method with an obvious advantage.</p>

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Keywords

Details 詳細情報について

  • CRID
    1390845713073510144
  • NII Article ID
    130007658327
  • DOI
    10.11517/pjsai.jsai2019.0_1q2j205
  • Text Lang
    ja
  • Data Source
    • JaLC
    • CiNii Articles
  • Abstract License Flag
    Disallowed

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