diplomsko delo
Kristjan Križman (Author), Jure Žabkar (Mentor)

Abstract

Primerjajte algoritma spodbujevanega učenja DQN in DDPG v danem simulacijskem okolju za parkiranje avtomobila. Znotraj omejitev simulatorja lahko spreminjate opise stanj in akcij tako, da bodo primerni za dana algoritma. Uporabite lahko obstoječe implementacije algoritmov ali razvijete svoje. Poročajte o uspešnosti obeh algoritmov, časovni zahtevnosti in njuni občutljivosti na začetne parametre.

Keywords

računalnik;simulacija;igra;avtomobil;učenje;visokošolski strokovni študij;diplomske naloge;

Data

Language: Slovenian
Year of publishing:
Typology: 2.11 - Undergraduate Thesis
Organization: UL FRI - Faculty of Computer and Information Science
Publisher: [K. Križman]
UDC: 004.8:004.94(043.2)
COBISS: 102689027 Link will open in a new window
Views: 101
Downloads: 17
Average score: 0 (0 votes)
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Other data

Secondary language: English
Secondary title: Comparison of two reinforcement learning algorithms in a car parking simulator
Secondary abstract: Compare the DQN and DDPG reinforcement learning algorithms in a given car parking simulation environment. You may change the descriptions of states and actions within the limits of the simulator, to suit the given algorithm. Use existing implementations or develop your own. Report on the performance of both algorithms, their time complexity and their sensitivity to initial parameters.
Secondary keywords: computer;machine learning;simulation;game;car;computer science;diploma;Strojno učenje;Računalniška simulacija;Računalništvo;Univerzitetna in visokošolska dela;
Type (COBISS): Bachelor thesis/paper
Study programme: 1000470
Embargo end date (OpenAIRE): 1970-01-01
Thesis comment: Univ. v Ljubljani, Fak. za računalništvo in informatiko
Pages: 41 str.
ID: 14808611