Predicting trajectories with Directed-Info GAIL
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Keywords
Imitation learning ; Directed-Info GAIL ; Variational Auto-Encoder ; Generative Adversarial Imitation Learning (GAIL)Abstract
As noted in the Directed-Info GAIL paper “the use of imitation learning to learn a single policy for a complex task that has multiple modes or hierarchical structure can be challenging”. This thesis will explore the use of Directed-Info GAIL algorithm, which is
based on the generative adversarial imitation learning framework to automatically learn subtask policies from unsegmented demonstrations of robot trajectories and aircraft trajectories, given that flights and robots have indeed different modes of behaviour in different segments of trajectories, depending on tasks they fulfil and many trajectories’ contextual features.
Postgraduate Studies Programme
Τεχνητή Νοημοσύνη - Artificial IntelligenceDepartment
Σχολή Τεχνολογιών Πληροφορικής και Επικοινωνιών. Τμήμα Ψηφιακών ΣυστημάτωνCorporate Department
Εθνικό Κέντρο Έρευνας Φυσικών Επιστημών «Δημόκριτος»Number of pages
93Language
EnglishCollections
Except where otherwise noted, this item's license is described as
Αναφορά Δημιουργού 3.0 Ελλάδα
Αναφορά Δημιουργού 3.0 Ελλάδα