A Deep-Reinforcement-Learning Approach to the
Peg-in-Hole Task with Goal Uncertainties

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A Deep-Reinforcement-Learning Approach to the Peg-in-Hole Task with Goal Uncertainties

The thesis proposed a framework to train deep-reinforcement-learning agents for fine manipulation tasks with goal uncertainties. It consisted of three aspects: state-space formulation, artificial training-goals uncertainties, and progressive training. The framework was used in a simulation for two f...

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Bibliographic Details
Main Author:	Rouillard, Thibault
Format:	Thesis
Published:	Curtin University 2020
Online Access:	http://hdl.handle.net/20.500.11937/85887

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