Reverse parking a car-like mobile robot with deep reinforcement learning and preview control

Eduardo Bejar, Antonio Moran Cardenas

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

8 Scopus citations

Abstract

This paper presents a control technique for reverse parking car-like vehicles based on deep reinforcement learning and preview control. The deep deterministic policy gradient (DDPG) algorithm is used for training a neurocontroller using a reward function defined in terms of the desired final state of the system. A preview control approach is employed to leverage knowledge of a known a priori reference input to generate a predictive control signal coupled into the neurocontroller output. Simulation results are presented to validate the proposed method. Moreover, these results show that incorporating a preview control signal improves the parking time.
Original languageSpanish
Title of host publication2019 IEEE 9th Annual Computing and Communication Workshop and Conference, CCWC 2019
Pages377-383
Number of pages7
StatePublished - 12 Mar 2019
Externally publishedYes

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