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Modelling human driving behaviour using Generative Adversarial Networks

Greveling, D.P. (2018) Modelling human driving behaviour using Generative Adversarial Networks. Master's Thesis / Essay, Computing Science.

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Abstract

In this thesis, a novel algorithm is introduced which combines Wasserstein Generative Adversarial Networks with Generative Adversarial Imitation Learning which is then applied to learning human driving behaviour. The focus of this thesis is to solve the problem of mode collapse and vanishing gradients from which Generative Adversarial Imitation Learning suffers and show that our implementation performs equally to the original Generative Adversarial Imitation Learning algorithm. The performance of the novel algorithm is evaluated on OpenAI Gym control problems and the NGSIM traffic dataset. The novel algorithm is shown to solve complex control problems on par with Generative Adversarial Imitation Learning and can learn to navigate vehicle trajectories.

Item Type: Thesis (Master's Thesis / Essay)
Degree programme: Computing Science
Thesis type: Master's Thesis / Essay
Language: English
Date Deposited: 15 Feb 2018 08:35
Last Modified: 15 Feb 2018 08:35
URI: http://fse.studenttheses.ub.rug.nl/id/eprint/16431

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