強化学習によるロボットの動作獲得のための基底関数に基づく行動空間生成手法

DOI Web Site 参考文献46件

書誌事項

タイトル別名
  • Constructing Action Space from Basis Functions for Motion Acquisition of Robots by Reinforcement Learning
  • キョウカ ガクシュウ ニ ヨル ロボット ノ ドウサ カクトク ノ タメ ノ キテイ カンスウ ニ モトズク コウドウ クウカン セイセイ シュホウ

この論文をさがす

抄録

Discrete action sets are often used in many reinforcement learning (RL) applications in robot control, since such sets are compatible with many RL methods and sophisticated architectures, such as Q(λ)-learning [1] and the Dyna. However, one of the problems is the absence of general principles on designing a discrete action set for robot control in higher dimensional input space. In this paper, we propose a discrete action set DCOB which is generated from the given basis functions (BFs) for approximating a value function. Though the DCOB is a discrete set, it has an ability to acquire high performance. Moreover, we utilize a method that generates a set of BFs based on the dynamics of the robot to reduce the number of the BFs. This way also makes the DCOB compact. Thus, the DCOB is compact and has an ability to acquire performance. Moreover, we also propose a method WF-DCOB, where the wire-fitting [2] is utilized to learn within a continuous action space which the DCOB discretizes. The purpose of the WF-DCOB is to evaluate the possibility of acquiring higher performance. Our proposition in the WF-DCOB is to constrain and initialize the parameters to relax the instability of the wire-fitting. We apply the proposed methods for a humanoid robot to learn crawling motion. The simulation results demonstrate outstanding advantages of the proposed method both in learning speed and ability to acquire performance, compared to conventional action space.

収録刊行物

参考文献 (46)*注記

もっと見る

関連プロジェクト

もっと見る

キーワード

詳細情報 詳細情報について

問題の指摘

ページトップへ