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Pregled bibliografske jedinice broj: 949233

Reinforcement learning in simulated systems


Livaja, Vladimir Dragutin
Reinforcement learning in simulated systems 2018., diplomski rad, diplomski, Fakultet elektrotehnike i računarstva, Zagreb


Naslov
Reinforcement learning in simulated systems

Autori
Livaja, Vladimir Dragutin

Vrsta, podvrsta i kategorija rada
Ocjenski radovi, diplomski rad, diplomski

Fakultet
Fakultet elektrotehnike i računarstva

Mjesto
Zagreb

Datum
09.07

Godina
2018

Stranica
50

Mentor
Pripužić, Krešimir

Ključne riječi
Podržano učenje, neuronske mreže ; agent ; okruženje ; Q vrijednosti ; vrijednost stanja ; povratne mreže ; iskusno ponavljanje
(Reinforcement learning ; agent ; environment ; deep neural networks ; Q values ; state values ; experience replay ; recurrent networks.)

Sažetak
In this thesis we focused on implementing a reinforcement agent that was able to generalize to various simulated systems and in doing so ; showed the ability of the reinforcement learning algorithm to adapt. Fundamentals of deep learning were also described. Using Tensorflow, a reinforcement learning agent was implemented. The agent was evaluated on three different scenarios: Pacman from the Atari 2600 games and two different scenarios of the game Doom. The evaluation results were shown via graphs.

Izvorni jezik
Engleski

Znanstvena područja
Elektrotehnika, Računarstvo



POVEZANOST RADA


Projekt / tema
HRZZ-UIP-2017-05-9066 - Učinkovita stvarnovremenska obrada brzih geoprostornih podataka (Krešimir Pripužić, )

Ustanove
Fakultet elektrotehnike i računarstva, Zagreb