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Drevesno preiskovanje Monte Carlo v porazdeljenem okolju : diplomsko delo
ID Grabnar, Jure (Author), ID Šter, Branko (Mentor) More about this mentor... This link opens in a new window, ID Lotrič, Uroš (Comentor)

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MD5: 678DCE8F62C85345E1A58E5C53498A76
PID: 20.500.12556/rul/7170eb8e-1a5f-41f8-bfb7-5726e29f05d8

Abstract
Algoritem drevesnega preiskovanja Monte Carlo (MCTS) je računsko precej zahteven, poleg tega pa čas računanja vpliva na kakovost rezultatov. Namen dela je zato paralelizacija metode MCTS. S paralelizacijo se poveča število iger in drugih parametrov - rezultati so boljši in bolj zanesljivi. Paralelni algoritem smo napisali s pomočjo knjižnice MPI, ki omogoča izvajanje na več računalnikih. Čas izvajanja algoritma smo merili na različnih velikostih problema. Rezultati paralelizacije so bili zadovoljivi, saj je bila pohitritev večinoma linearna. Algoritem smo izvajali na omrežju grid, za katerega skrbi Slovenska iniciativa za nacionalni grid. V okviru dela so nastala tudi navodila za uporabo omrežja grid.

Language:Slovenian
Keywords:drevesno preiskovanje Monte Carlo, porazdeljeni sistemi, SLING, umetna inteligenca, MPI
Work type:Bachelor thesis/paper
Typology:2.11 - Undergraduate Thesis
Organization:FRI - Faculty of Computer and Information Science
Publisher:[J. Grabnar]
Year:2015
Number of pages:35 str.
PID:20.500.12556/RUL-72441 This link opens in a new window
COBISS.SI-ID:1536568771 This link opens in a new window
Publication date in RUL:17.09.2015
Views:1756
Downloads:500
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Secondary language

Language:English
Title:Monte Carlo tree search in a distributed environment
Abstract:
Monte Carlo Tree Search algorithm (MCTS) is a computationally expensive algorithm. The time needed for computation correlates with the quality of the results. The purpose of this work is to parallelize MCTS method. With parallelization we gain an ability to increase the number of simulated games per turn and other parameters and still be able to receive results in sufficient time. Quality of results has been improved significantly. Parallel algorithm was written in MPI library which enables the program to run on multiple computers. Algorithm was evaluated on different problem sizes. With big enough problem, the speedup was approximately linear. Algorithm was run on a grid network which is administered by Slovenian Initiative for National Grid (SLING). As a part of this work, instructions for usage of grid network were created.

Keywords:Monte Carlo Tree Search, distributed systems, SLING, artificial intelligence, MPI

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