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Deep reinforcement learning methods

Deep reinforcement learning methods

Abstract

Το αντικείμενο της παρούσας διατριβής είναι οι μέθοδοι Βαθιάς Ενισχυτικής Μάθησης (Deep Reinforcement Learning). Η διατριβή εξερευνά υπάρχουσες τεχνικές Βαθιάς Ενισχυτικής Μάθησης, ενώ επίσης παρουσιάζει νέες, οι οποίες έχουν βελτιωμένες επιδόσεις σε κοινά περιβάλλοντα αξιολόγησης σε σύγκριση με μεθόδους αιχμής της σχετικής βιβλιογραφίας, ενώ ταυτόχρονα βελτιώνουν συγκεκριμένες πτυχές τους, και πιο συγκεκριμένα, τη δειγματική αποδοτικότητα (sample-efficiency) και την ικανότητα γενίκευσης (generalization). Τέλος, παρουσιάζονται διάφορες εφαρμογές τεχνικών Μηχανικής Μάθησης σε παιχνίδια.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
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