A parallel heterogeneous policy deep reinforcement learning algorithm for bipedal walking motion design

Considering the dynamics and non-linear characteristics of biped robots, gait optimization is an extremely challenging task. To tackle this issue, a parallel heterogeneous policy Deep Reinforcement Learning (DRL) algorithm for gait optimization is proposed. Firstly, the Deep Deterministic Policy Gra...

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Bibliografische Detailangaben

Hauptverfasser:	Chunguang Li, Mengru Li, Chongben Tao
Format:	Artigo
Sprache:	Inglês
Veröffentlicht:	Frontiers Media S.A. 2023-08-01
Schriftenreihe:	Frontiers in Neurorobotics
Schlagworte:	gait optimization biped robot Deep Deterministic Policy Gradient experience replay parallel heterogeneous strategy
Online-Zugang:	https://www.frontiersin.org/articles/10.3389/fnbot.2023.1205775/full
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